基于稀疏表示的图像融合与去噪算法研究
|
摘要
稀疏表示用尽可能少的原子表示信号的主要信息,已经受到了广泛关注。研究快速有效的稀疏表示算法及其在图像处理中的应用具有重要的理论研究意义和实际应用价值。本文从稀疏表示理论出发,深入研究稀疏性在图像处理中的应用方法,以字典训练算法和稀疏模型的构建方法作为研究重点,对基于稀疏表示的图像融合与去噪等问题进行了探索性和创新性的研究。主要工作包括以下几个方面：
(1)研究解决了基于K-SVD (K-singular value decomposition)字典训练算法的图像融合问题。针对CT和MRI图像,提出一种基于K-SVD字典训练算法的图像特征提取和融合算法,通过使用K-SVD算法,增强了融合算法的特征提取和抗噪声干扰能力。使用最大化选择规则合并稀疏系数中的非零元素从而对图像特征分别进行融合,提高了图像的清晰性和对医学图像细节特征的保留能力。
针对现有的空间域和变换域融合算法中存在的优缺点,提出一种基于联合K-SVD字典训练的稀疏域和空间域相结合的图像融合技术。通过使用联合K-SVD字典训练算法,得到与每个图像相对应的字典,将字典中的原子看作图像特征,选用适当的物理特性分别进行合并实现图像融合,解决了空间域算法中存在的图像清晰度下降、易受噪声干扰和变换域算法中存在的物理意义不明确等问题。
(2)研究解决了基于稀疏表示和Piella指数优化的图像融合问题。针对图像融合过程和评价过程相脱节的情况,提出一种基于Piella (?)旨数优化的图像融合算法,克服了融合过程中的盲目性,得到了Piella指数最优的融合图像。针对压缩感知条件下的图像融合问题,提出一种压缩感知条件下的基于Piella指数优化的融合算法。该算法只需要进行一次完整的图像重构,即可获得较好的融合效果,并在一定程度上减小了计算量。
针对Piella指数优化过程中存在的易受噪声干扰和计算量较大的问题,提出基于稀疏表示和Piella (?)旨数优化的图像融合算法。该算法结合稀疏表示和Piella指数优化技术,代替稀疏系数,对字典中的原子使用Piella指数优化算法进行合并,由于字典的维数通常小于稀疏系数的个数,因此该算法可以减少计算量。利用稀疏表示的抗噪声干扰能力,增强了算法的鲁棒性,当原始图像含有噪声时,融合图像也能得到较高的Piella指数值。
(3)研究解决了基于联合稀疏表示的图像融合与去噪问题。受到分布式压缩感知的启发,研究稀疏表示在多重信号中的应用,提出联合稀疏表示算法,可以同时计算多重信号的公共稀疏系数和各自稀疏系数。山于多个待融合图像是不同传感器对同一目标的观测,因此它们之间存在公共特征,而每一个图像中包含其各自特征,因此使用最大化选择规则将丢失大量各自特征,而加权平均规则将使融合图像中各自特征比例下降。针对这一问题,提出一种基于联合稀疏表示的图像特征提取和融合算法,使用联合稀疏表示算法同时提取并分离公共特征和各自特征,通过对其分别进行合并得到的融合图像中完整保留了图像特征并增强了重要特征的清晰度。
针对多重图像受到稀疏噪声污染的问题,提出基于联合稀疏表示的多重图像去噪算法。联合稀疏表示算法较好的应用了多重图像之间的相关性,将其中的公共成分看作去噪图像,各自成分看作噪声,并利用联合稀疏表示加以分离,从而实现图像去噪。经典的稀疏表示去噪算法通常假设噪声是非稀疏的,而当噪声具有稀疏性时,去噪效果将大大下降。本文算法针对稀疏噪声,是对经典算法的有益补充。
Sparse representation (SR) that desires to represent the signal with the least atoms, has been recognized widely. It's theoretically and practically important to do research on fast sparse representation algorithms in image representation and image processing. This paper takes SR theory as basis, deeply analyzes its application in image processing, focuses on the methods of learning dictionary and constructing the sparse model, and makes an exploratory and innovative study of SR based image processing technology, including image fusion and denoising. The main contents of the paper include:
(1) The technology of image features extraction and fusion based on K-SVD (K-singular value decomposition) is studied. In the light of CT-MRI image fusion, an image features extraction and fusion method with K-SVD is presented. By using the K-SVD algorithm, the capabilities of image feature extraction and denoising are strengthen. This method combines the non-zero elements with the "choose-max" rule to fuse the image features separately, so that the definition of image and the capability of preserving the detailed features of the medical image can be improved.
Both existing spatial domain and transform domain fusion methods have their own advantages and disadvantages. A combined sparse-spatial representation method based on joint K-SVD is proposed. Firstly, the dictionary corresponding to each image is learned by joint K-SVD. Secondly, the atoms of the dictionary are taken as the image features and combined with the appropriate physical properties to acquire the fused image. The new method can overcome the disadvantages of the definition of image declining and weaker anti-noise-interference ability in spartial domain methods, and lacking the definite physical meanings in transform domain methods.
(2) The technology of image fusion based on SR and Piella index optimization is studied. To integrate the processes of image fusion and evaluation, a novel image fusion method with Piella index optimization is proposed. This method can get rid of the blindness in process of image fusion and acquire the optimal fused image in the light of Piella index. To the fusion technology in CS. a novel image fusion algorithm based on Piella index optimization in CS is proposed. This method only needs one complete reconstruction of image to acquire the fused image with a good effect, so it can reduce computation to some extent.
The Piella index optimization algorithm is sensitive to noise and has high computation cost. To solve these problems, an image fusion method with SR and Piella index optimization is presented. This method combines the techniques of the SR and Piella index optimization. The atoms of the dictionaries substituting for sparse coefficients are fused with Piella index optimization algorithm. The dimension of the dictionary is usually less than that of the coefficient matrix, so our method can offer a reduction in the computational complexity. Since SR has stronger ability to remove noise, our method is naturally robust to noise. Even if the original images are corrupted with noise, the fused image acquired by our method is still high on Piella index.
(3) The technology of image fusion and denoising based on joint sparse representation (JSR) algorithm is studied. Inspired by distributed compressed sensing (DCS), we research the SR algorithm for multiple signals and propose the JSR algorithm. It can calculate the common and unique sparse coefficients of the multiple signals simultaneously. Since the sensors presumably observe related phenomena, the ensemble of signals they acquire can be expected to possess some joint structure, or correlation. The features of each image are generated as combination of two components:the common component, which is present in all images, and the unique component, which is unique to each image. So with the "choose-max" rule, a lot of unique features are discarded, and with the "weighted average" rule, the ratio of weights between each unique feature and the common feature of the fused image drops. To solve these problems, we present an image features extraction and fusion method based on JSR. Since this method can separate the common and unique features of source images and fuse them separately, the fused image can preserve the image features completely and enhance the clarity of the significant features.
In order to recover the original images from multiple copies corrupted with the sparse noises, a denoising algorithm with JSR is presented. JSR makes good use of the correlation among the multiple image copies well. All copies share a common component—the image, while each individual measurement contains a unique component—the noise. JSR can separate the common and unique components to denoise the images. The classical denoising algorithm with SR assumes that the noise is non-sparse. It performs suboptimally when the noise is sparse in some dictionary. This method addresses the recovery of original images from multiple copies corrupted with the sparse noises, and it is a useful addition to the classical algorithm.
(1)研究解决了基于K-SVD (K-singular value decomposition)字典训练算法的图像融合问题。针对CT和MRI图像,提出一种基于K-SVD字典训练算法的图像特征提取和融合算法,通过使用K-SVD算法,增强了融合算法的特征提取和抗噪声干扰能力。使用最大化选择规则合并稀疏系数中的非零元素从而对图像特征分别进行融合,提高了图像的清晰性和对医学图像细节特征的保留能力。
针对现有的空间域和变换域融合算法中存在的优缺点,提出一种基于联合K-SVD字典训练的稀疏域和空间域相结合的图像融合技术。通过使用联合K-SVD字典训练算法,得到与每个图像相对应的字典,将字典中的原子看作图像特征,选用适当的物理特性分别进行合并实现图像融合,解决了空间域算法中存在的图像清晰度下降、易受噪声干扰和变换域算法中存在的物理意义不明确等问题。
(2)研究解决了基于稀疏表示和Piella指数优化的图像融合问题。针对图像融合过程和评价过程相脱节的情况,提出一种基于Piella (?)旨数优化的图像融合算法,克服了融合过程中的盲目性,得到了Piella指数最优的融合图像。针对压缩感知条件下的图像融合问题,提出一种压缩感知条件下的基于Piella指数优化的融合算法。该算法只需要进行一次完整的图像重构,即可获得较好的融合效果,并在一定程度上减小了计算量。
针对Piella指数优化过程中存在的易受噪声干扰和计算量较大的问题,提出基于稀疏表示和Piella (?)旨数优化的图像融合算法。该算法结合稀疏表示和Piella指数优化技术,代替稀疏系数,对字典中的原子使用Piella指数优化算法进行合并,由于字典的维数通常小于稀疏系数的个数,因此该算法可以减少计算量。利用稀疏表示的抗噪声干扰能力,增强了算法的鲁棒性,当原始图像含有噪声时,融合图像也能得到较高的Piella指数值。
(3)研究解决了基于联合稀疏表示的图像融合与去噪问题。受到分布式压缩感知的启发,研究稀疏表示在多重信号中的应用,提出联合稀疏表示算法,可以同时计算多重信号的公共稀疏系数和各自稀疏系数。山于多个待融合图像是不同传感器对同一目标的观测,因此它们之间存在公共特征,而每一个图像中包含其各自特征,因此使用最大化选择规则将丢失大量各自特征,而加权平均规则将使融合图像中各自特征比例下降。针对这一问题,提出一种基于联合稀疏表示的图像特征提取和融合算法,使用联合稀疏表示算法同时提取并分离公共特征和各自特征,通过对其分别进行合并得到的融合图像中完整保留了图像特征并增强了重要特征的清晰度。
针对多重图像受到稀疏噪声污染的问题,提出基于联合稀疏表示的多重图像去噪算法。联合稀疏表示算法较好的应用了多重图像之间的相关性,将其中的公共成分看作去噪图像,各自成分看作噪声,并利用联合稀疏表示加以分离,从而实现图像去噪。经典的稀疏表示去噪算法通常假设噪声是非稀疏的,而当噪声具有稀疏性时,去噪效果将大大下降。本文算法针对稀疏噪声,是对经典算法的有益补充。
Sparse representation (SR) that desires to represent the signal with the least atoms, has been recognized widely. It's theoretically and practically important to do research on fast sparse representation algorithms in image representation and image processing. This paper takes SR theory as basis, deeply analyzes its application in image processing, focuses on the methods of learning dictionary and constructing the sparse model, and makes an exploratory and innovative study of SR based image processing technology, including image fusion and denoising. The main contents of the paper include:
(1) The technology of image features extraction and fusion based on K-SVD (K-singular value decomposition) is studied. In the light of CT-MRI image fusion, an image features extraction and fusion method with K-SVD is presented. By using the K-SVD algorithm, the capabilities of image feature extraction and denoising are strengthen. This method combines the non-zero elements with the "choose-max" rule to fuse the image features separately, so that the definition of image and the capability of preserving the detailed features of the medical image can be improved.
Both existing spatial domain and transform domain fusion methods have their own advantages and disadvantages. A combined sparse-spatial representation method based on joint K-SVD is proposed. Firstly, the dictionary corresponding to each image is learned by joint K-SVD. Secondly, the atoms of the dictionary are taken as the image features and combined with the appropriate physical properties to acquire the fused image. The new method can overcome the disadvantages of the definition of image declining and weaker anti-noise-interference ability in spartial domain methods, and lacking the definite physical meanings in transform domain methods.
(2) The technology of image fusion based on SR and Piella index optimization is studied. To integrate the processes of image fusion and evaluation, a novel image fusion method with Piella index optimization is proposed. This method can get rid of the blindness in process of image fusion and acquire the optimal fused image in the light of Piella index. To the fusion technology in CS. a novel image fusion algorithm based on Piella index optimization in CS is proposed. This method only needs one complete reconstruction of image to acquire the fused image with a good effect, so it can reduce computation to some extent.
The Piella index optimization algorithm is sensitive to noise and has high computation cost. To solve these problems, an image fusion method with SR and Piella index optimization is presented. This method combines the techniques of the SR and Piella index optimization. The atoms of the dictionaries substituting for sparse coefficients are fused with Piella index optimization algorithm. The dimension of the dictionary is usually less than that of the coefficient matrix, so our method can offer a reduction in the computational complexity. Since SR has stronger ability to remove noise, our method is naturally robust to noise. Even if the original images are corrupted with noise, the fused image acquired by our method is still high on Piella index.
(3) The technology of image fusion and denoising based on joint sparse representation (JSR) algorithm is studied. Inspired by distributed compressed sensing (DCS), we research the SR algorithm for multiple signals and propose the JSR algorithm. It can calculate the common and unique sparse coefficients of the multiple signals simultaneously. Since the sensors presumably observe related phenomena, the ensemble of signals they acquire can be expected to possess some joint structure, or correlation. The features of each image are generated as combination of two components:the common component, which is present in all images, and the unique component, which is unique to each image. So with the "choose-max" rule, a lot of unique features are discarded, and with the "weighted average" rule, the ratio of weights between each unique feature and the common feature of the fused image drops. To solve these problems, we present an image features extraction and fusion method based on JSR. Since this method can separate the common and unique features of source images and fuse them separately, the fused image can preserve the image features completely and enhance the clarity of the significant features.
In order to recover the original images from multiple copies corrupted with the sparse noises, a denoising algorithm with JSR is presented. JSR makes good use of the correlation among the multiple image copies well. All copies share a common component—the image, while each individual measurement contains a unique component—the noise. JSR can separate the common and unique components to denoise the images. The classical denoising algorithm with SR assumes that the noise is non-sparse. It performs suboptimally when the noise is sparse in some dictionary. This method addresses the recovery of original images from multiple copies corrupted with the sparse noises, and it is a useful addition to the classical algorithm.
引文
[1]Ghazel M, Freeman G H, Vrscay E R. Fractal-wavelet image denoising revisited [J]. IEEE Transactions on Image Processing,2006,15(9):2669-2675.
[2]Yu H, Zhao L, Wang H. Image denoising using trivariate shrinkage filter in the Wavelet domain and joint bilateral filter in the spatial domain [J]. IEEE Transactions on Image Processing,2009,18(10): 2364-2369.
[3]Zhang L, Li X, Zhang D. Image denoising and zooming under the linear minimum mean square-error estimation framework [J]. IET Image Processing,2012,6(3):273-283.
[4]Kasprzak W, Cichocki A. Hidden image separation from incomplete image mixtures by independent component analysis [J]. Proceedings of the 13th International Conference on Pattern Recognition,1996, 2:394-398.
[5]Tonazzini A, Bedini L, Salerno E. A Markov model for blind image separation by a mean-field EM algorithm [J]. IEEE Transactions on Image Processing,2006,15(2):473-482.
[6]Be'ery E, Yeredor A. Blind separation of superimposed shifted images using parameterized joint diagonalization [J]. IEEE Transactions on Image Processing,2008,17(3):340-353.
[7]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature,1996,381(6583):607-609.
[8]Vinje W E, Gallant J L. Sparse coding and decorrelation in primary visual cortex during natural vision [J]. Science,2000,287:1273-1276.
[9]Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition [C]. Asilomar Conf. Signals, Syst. Comput., 1993,1.
[10]Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit [J]. SI AM Rev.,2001, 43(1):129-159.
[11]Harmany Z T, Marcia R F, Willett R M. This is SPIRAL-TAP:Sparse Poisson Intensity Reconstruction ALgorithms-Theory and Practice [J]. IEEE Transactions on Image Processing,2010, 21(3):1084-1096.
[12]Candes E, Romberg J, Tao T. Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information [J]. IEEE Trans. on Information Theory,2006,52(2):489-509.
[13]Candes E. Compressive Sampling [J]. Int. Congress of Mathematics,2006,3:1433-1452.
[14]Baraniuk R. Compressive sensing [J]. IEEE Signal Processing Magazine,2007,24(4):118-121.
[15]Candes E, Wakin M. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008,25(2):21-30.
[16]Liu Z, Blasch E, Xue Z, et.al. Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision:A comparative study[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(1):94-109.
[17]Youshen X, Kamel M S. Novel cooperative neural fusion algorithms for image restoration and image fusion [J]. IEEE Transactions on Image Processing,2007.16(2):367-381.
[18]Xu M, Chen H, Varshney P K. An image fusion approach based on markov random fields [J]. IEEE Transactions on Geoscience and Remote Sensing,2011,49(12):5116-5127.
[19]Matsopoulos G K, Marshall S, Brunt J N H. Multiresolution morphological fusion of MR and CT images of the human brain Vision [J], IEE Proceedings Image and Signal Processing,1994,141(3): 137-142.
[20]Zhang Q, Guo B. Infrared and Color Visible Images Fusion Based on Second Generation Curvelet Transform [J]. Industrial Electronics and Applications,2007:2118-2123.
[21]Kong W, Lei Y, Ni X. Fusion technique for grey-scale visible light and infrared images based on non-subsampled contourlet transform and intensity-hue-saturation transform [J]. IET Signal Processing,2011,5(1):75-80.
[22]Agrawal D, Singhai J. Multifocus image fusion using modified pulse coupled neural network for improved image quality [J]. IET Image Processing,2010,4(6):443-451.
[23]Wang W W, Shui P L, Song G X. Multifocus image fusion in wavelet domain Machine [C]. International Conference on Learning and Cybernetics,2003,5:2887-2890.
[24]Xue X, Lei G, Wang H, et. al. A parallel fusion method of remote sensing image based on IHS transformation [C]. International Congress on Image and Signal Processing (CISP),2011,3: 1600-1603.
[25]Farah I R, Boulila W, Ettabaa K S, et.al. Interpretation of multisensor remote sensing images: multiapproach fusion of uncertain information [J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(12):4142-4152.
[26]Kempeneers P, Sedano F, Seebach L, et.al. Data fusion of different spatial resolution remote sensing images applied to forest-type mapping [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(12):4977-4986.
[27]Rehman A, Wang Z, Brunei D, et.al. SSIM-inspired image denoising using sparse representations Acoustics [C]. IEEE International Conference on Speech and Signal Processing (ICASSP),2011: 1121-1124.
[28]Gilboa G, Sochen N, Zeevi Y Y. Variational denoising of partly textured images by spatially varying constraints [J]. IEEE Transactions on Image Processing,2006,15(8):2281-2289.
[29]Kivanc M M, Kozintsev I, Ramchandran K, et.al. Low-complexity image denoising based on statistical modeling of wavelet coefficients [J]. IEEE Signal Processing Letters,1999,6(12):300-303.
[30]Zheng Y, Fu H, Au O K C, et.al. Bilateral normal filtering for mesh denoising [J]. IEEE Transactions on Visualization and Computer Graphics,2011,17(10):1521-1530.
[31]Duarte-Carvajalino J M, Sapiro G, Velez-Reyes M, et.al. Multiscale representation and segmentation of hyperspectral imagery using geometric partial differential equations and algebraic multigrid methods [J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(8):2418-2434.
[32]Liu W, Ma Z. Wavelet Image threshold denoising based on edge detection [C]. IMACS Multiconference on Computational Engineering in Systems Applications,2006,1:72-78.
[33]Ghazel M, Freeman G H, Vrscay E R. Fractal-wavelet image denoising [J]. International Conference on Image Processing,2002,1:1-836-1-839.
[34]Shui P L, Zhou Z F, Li J X. Image denoising algorithm via best wavelet packet base using Wiener cost function [J]. IET Image Processing.2007,1(3):311-318.
[35]Lu J, Zou Y, Ye Z. Enhanced fractal-wavelet image denoising [C]. ISECS International Colloquium on Computing, Communication, Control, and Management,2008,1:115-119.
[36]Shidahara M, Ikoma Y, Seki C, et.al. Wavelet denoising of dynamic PET data:Application to the parametric imaging of peripheral benzodiazepine receptor [C]. IEEE Nuclear Science Symposium Conference Record,2006,1:3217-3220.
[37]Yu H, Zhao L, Wang H. Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain [J]. IEEE Transactions on Image Processing,2009,18(10): 2364-2369.
[38]Chang S G, Bin Y, Vetterli M. Multiple copy image denoising via wavelet thresholding [C]. International Conference on Image Processing,1998,1:545-549.
[39]Liang T, Zheng Z, Lei S, et.al. Laplace prior based distributed compressive sensing [C]. Internationa! ICST Communications and Networking in China (CHINACOM),2010:1-4.
[40]Zeng F, Chen L, Zhi T. Distributed compressive spectrum sensing in cooperative multihop cognitive networks [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(1):37-48.
[41]Aslantas V, Kurban R. Fusion of multi-focus images using differential evolution algorithm [J]. Expert Systems with Applications,2010,37(12):8861-8870.
[42]Maruthi R. Spatial domain method for fusing multi-focus images using measure of fuzziness [J]. International Journal of Computer Applications,2011,20(7):48-51.
[43]Oudre L, Stathaki T, Mitianoudis N. Image fusion using optimization of statistical measurements [M]. Book chapter. Image fusion:Algorithms and Applications, Academic Press.2008:251-272.
[44]Li S, Kwok J T, Wang Y. Combination of images with diverse focuses using the spatial frequency [J]. Information Fusion,2001,2(3):169-176.
[45]Zhang Y, Ge L. Efficient fusion scheme for multi-focus images by using blurring measure [J]. Digital Signal Processing,2009,19(2):186-193.
[46]Petrovic V S, Xydeas C S. Gradient-dased multiresolution image fusion [J]. IEEE Transactions on image processing,2004,13(2):228-237.
[47]Ren H, Lan Y, Zhang Y. Research of multi-focus image fusion based on M-band multi-wavelet transformation [C]. Advanced Computational Intelligence (IWACI),2011:395-398.
[48]Ye Z, Mohamadian H, Ye Y. Sensing data discrete wavelet fusion for pattern recognition with qualitative and quantitative measuring [J]. Neural Networks,2008:3647-3652.
[49]Ma J, Gong M, Zhou Z. Wavelet fusion on ratio images for change detection in SAR images [J]. IEEE Geoscience and Remote Sensing Letters,2012,9(6):1122-1126.
[50]Ranchin T, Wald L. Data fusion of remotely sensed images using the wavelet transform:the ARSIS solution [C]. Proceedings, SPIE Annual Meeting, International Symposium on Optical Science, Engineering and Instrumentation,1997,3169:272-280.
[51]Chipman L J, Orr T M, Graham L N. Wavelets and image fusion [C]. International Conference on Image Processing, Washington,1995:248-251.
[52]Prakash N K. Image fusion algorithm based on biorthogonal wavelet [J]. International Journal of Enterprise Computing and Business System International Systems,2011,1(2)
[53]Roshni V S. Mutual information based registration and region based wavelet fusion of images [J]. Computer Vision, Graphics & Image Processing,2008:606-613
[54]Kim Y. Improved Additive-wavelet image fusion [J]. IEEE Geoscience and Remote Sensing Letters, 2011,8(2):263-267.
[55]曹玉珍,刘晓婷等.基于凸集投影与小波融合的图像超分辨率重建方法[J].生物医学工程学杂志,2009,26(5)：3947-951.
[56]周礼,王章野,金剑秋等.基于HVS的小波图像融合新算法[J].中国图像图形学报,2004,9(9)：1088-1094.
[57]Zhang H. A novel medical image fusion method [C]. International Conference on Multimedia and Ubiquitous Engineering,2007:548-553
[58]Cai X. Wavelet image fusion based on the high order polynomial regression [C]. IEEE International Geoscience and Remote Sensing Symposium,2007:3100-3103
[59]Omar Z, Mitianoudis N, Stathaki T. Region-based image fusion using a combinatory Chebyshev-ICA method [C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011:1213-1216
[60]Hyvarinen A, Hoyer P, Inki M. Topographic ICA as a model of V1 receptive fields [C]. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks,2000,4:83-88.
[61]Mitianoudis N, Stathaki T. Pixel-based and region-based image fusion schemes using ICA bases [J]. Information Fusion,2007,8(2):131-142.
[62]Mitianoudis N, Stathaki T. A daptive image fusion using ICA bases [C]. IEEE International Conference on Acoustics, Speech and Signal Processing,2006:1-4
[63]Yang B, Li S. Multifocus Image fusion and restoration with sparse representation [J]. IEEE Trans. Instrumentation and Measurement,2010,59(4):884-891.
[64]Hu J, Li S, Yang B. Remote sensing image fusion based on IHS transform and sparse representation [C]. Chinese Conference on Pattern Recognition (CCPR),2010:1-4.
[65]Yang B, Li S. Pixel-level image fusion with simultaneous orthogonal matching pursuit [J]. Information Fusion,2012:10-19
[66]Tropp J A, Gilbert A C, Strauss M J. Algorithms for simultaneous sparse approximation [J]. Signal Processing,2006,86 (3):572-588.
[67]Wang Q, Shen Y, Jin J. Performance evaluation of image fusion techniques [J]. Image Fusion: Algorithms and Applications, ch.19, T. Stathaki, ed., pp. Elsevier,2008:469-492.
[68]Hossny M, Nahavandi S, Vreighton D. Comments on "Information Measure for Performance of Image Fusion" [J]. Electronics Letters,2008,44(18):1066-1067.
[69]Xydeas C, Petrovic V. Objective image fusion performance measure [J]. Electronic Letter,2000,6(4): 308-309.
[70]Piella G, Heijmans H. A new quality metric for image fusion [C]. Image Processing. Spain:IEEE, 2003:173-176.
[71]Cvejic N, Loza A. Bull D, et.al. A similarity metric for assessment of image fusion algorithms [J]. International Journal of Signal Processing,2005.2(3):178-182.
[72]胡浩,王明照.杨杰自适应模糊加权均值滤波器[J].系统工程与电子技术,2002,24(2)：15-19.
[73]柴宝仁.中值滤波在气象传真图中降下噪的分析[J].北京理工大学学报,2011,1(31)：417-419
[74]胡小平,陈国良,毛征宇等.离焦模糊图像的维纳滤波复原研究[J].仪器仪表学报,2007,28(3)：479-483.
[75]聂坚,郑金华,谢泞志等.傅里叶空间变换处理带噪声进化算法的研究[J].计算机工程与应用,2011,47(28)：33-36
[76]Xia R, MENG K, QIAN F, et.al. Online wavelet denoising via a moving window original research Article [J]. Acta Automatica Sinica,2007,33(9):897-901.
[77]Park H, Lee T. Capturing nonlinear dependencies in natural images using ICA and mixture of Laplacian distribution [J]. Neurocomputing,2006,69(13-15):1513-1528.
[78]Aharon M, Elad M, Bruckstein A. K-SVD:An algorithm for designing overcomplete dictionaries for sparse representation [J]. IEEE Trans. Signal Process.,2006,54(11):4311^4322.
[79]Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries [J]. IEEE Trans, Image Process.,2006,15(12):3736-3745.
[80]Laska J N. Exact signal recovery from sparsely corrupted measurements through the Pursuit of Justice [J]. Signals, Systems and Computers,2009:1556-1560
[81]Chang G, Yu B, Vetterli M. Wavelet thresholding for multiple noisy image copies [J]. IEEE Transactions on Image Processing,2000,9(9):1631-1635.
[82]Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries [J]. IEEE Transactions on Signal Processing,1993,41(12):3397-3415.
[83]Duarte M, Davenport M, Takhar D, et.al. Single-pixel imaging via compressive sampling [J]. IEEE Signal Processing Magazine,2008,25(2):83-91.
[84]Chan W L, Moravec M, Baraniuk R, et.al. Terahertz imaging with compressed sensing and phase retrieval [J]. Optics Letters,2008,33:974-976.
[85]Coskun A F, Su T, Sencan I, et.al. Lensfree fluorescent on-chip Imaging using compressive sampling [J]. Optics & Photonics News,2010,21(12):27-27.
[86]Pati Y C. Orthogonal matching pursuit:recursive function approximation with applications to wavelet decomposition [C]. Proceedings of the 27 th Annual Asilomar Conference on Signals, Systems, and Computers,1993,1:40-44.
[87]Candes E, Michael W. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine,2008,25(2):21-30.
[88]Romberg J. Imaging via compressive sampling [J]. IEEE Signal Processing Magazine,2008,25(2):14-20.
[89]Angelosante D, Giannakis G B. RLS-weighted Lasso for adaptive estimation of sparse signals [C]. IEEE International Conference on Acoustics. Speech and Signal Processing,2009:3245-3248.
[90]He Z, He Z, Cichocki A, et.al. Improved FOCUSS method with conjugate gradient iterations [J]. IEEE Transactions on Signal Processing,2009,57(1):399-404.
[91]Chartrand R. Exact reconstructions of sparse signals via nonconvex minimization[J]. IEEE Signal Proc. Lett.,2007,14(10):707-710.
[92]Mairal J, Elad M, Sapiro G. Sparse representation for color image restoration [J]. IEEE Transactions on Image Processing,2008,17(1):53-69.
[93]Yang J, Wright J, Huang T S, et.al. Image super-resolution via sparse representation [J]. IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[94]Panagakis Y, Kotropoulos C. Music genre classification via topology preserving non-negative tensor factorization and sparse representations [C]. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010:249-252.
[95]Ji Y, Lin T, Zha H. Mahalanobis distance based non-negative sparse representation for face recognition [C]. International Conference on Machine Learning and Applications,2009:41-46.
[96]Kumar M, Loui A C. Key frame extraction from consumer videos using sparse representation [C]. IEEE International Conference on Image Processing (ICIP),2011:2437-2440
[97]Drajic D, Cvejic N. Multimodal image fusion in presence of noise using sparse coding of ICA [C]. IEEE International Symposium on Signal Processing and Information Technology,2007:343-346.
[98]李志清,施智平,李志欣等.结构相似度稀疏编码及其图像特征提取[J],模式识别与人工智能,2010,23(1)：17-22.
[99]Nirenberg S, Carcieri S M, Jacobs A L, et.al. Retinal ganglion cells act largely as independent encoders [J]. Nature,2001,411:698-701.
[100]邓承志.图像稀疏表示理论及其应用研究[D].华中科技大学,博士学位论文.
[101]Donoho D, Huo X. Beamlets and multiscale image analysis [J]. Springer Lecture Notes in Computational Science and Engineering,2002,20:149-196.
[102]Bruckstein A M, Elad M, Zibulevsky M. Sparse non-negative solution of a linear system of equations is unique [C].3rd International Symposium on Communications, Control and Signal Processing,2008: 762-767.
[103]Ben-Haim Z, Eldar Y C. Near-oracle performance of greedy block-sparse estimation techniques from noisy measurements [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(5):1032-1047.
[104]Duarte M, Sarvotham S, Baron D, et.al. Distributed compressed sensing of jointly sparse signals [J]. in Asilomar Conf. Signals, Systems and Computers, (Pacific Grove, CA),2005:1-4.
[105]Baron D, Duarte M, Wakin M, et.al. Distributed compressive sensing [C]. in Proceedings of the Sensor, Signal and Information Processing Workshop,2008:1-4.
[106]曾绍华.LS-SVM的组合优化算法研究[J].计算机工程与应用,2007,43(22)：89-92.
[107]陈发宇,尚永生,杨长春.Matching Pursuits方法综述[J].地球物理学进展,2007,22(5)：1466-1473.
[108]Padberg M, Rinaldi G. A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems [J], Industrial and Applied Mathematics,1991,33(1):60-100
[109]Qiu Q, Jiang Z, Chellappa R. Sparse dictionary-based representation and recognition of action attributes [C]. IEEE International Conference on Computer Vision (ICCV).2011:707-714.
[110]Lee K, Tak S, Ye J C. A data-driven dparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion [J]. IEEE Transactions on Medical Imaging,2011,30(5):1076-1089.
[111]Yang C, Peng J, Fan J. Image collection summarization via dictionary learning for sparse representation [J]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2012: 1122-1129.
[112]Horev I, Bryt O, Rubinstein R. Adaptive image compression using sparse dictionaries [C].19th International Conference on Systems, Signals and Image Processing (IWSSIP),2012:592-595.
[113]Jafari M G, Plumbley M D, Davies M E. Speech separation using an adaptive sparse dictionary algorithm [C]. Hands-Free Speech Communication and Microphone Arrays,2008:25-28
[114]Jajamovich G H, Wang X. Haplotype inference based on sparse dictionary selection [C]. Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011:1021-1025.
[115]Lee K., Ye J C. Statistical parametric mapping of FMR1 data using sparse dictionary learning [C]. IEEE International Symposium on Biomedical Imaging:From Nano to Macro,2010:660-663.
[116]Eavani H, Filipovych R, Davatzikos C, et.al Sparse dictionary learning of resting state fMRI networks [C]. International Workshop on Pattern Recognition in NeuroImaging (PRNI),2012:73-76.
[117]Jolliffe I T. Principal component analysis [C]. Springer, New York:2002:1-4.
[118]Engan K, Aase S O, Husoy J H. Multi-frame compression:theory and design [J]. EURASIP Signal Process.,2000,80(10):2121-2140.
[119]Engan K, Skretting K, Husoy J H. Denoising of images using designed signal dependent frames and matching pursuit [C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005,2:653-656.
[120]Tron R, Vidal R. Distributed computer vision algorithms through distributed averaging [C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2011:57-63.
[121]Rubinstein R, Zibulevsky M, Elad M. Double sparsity:learning sparse dictionaries for sparse signal approximation [J]. IEEE Transactions on Signal Processing,2010,58(3):1553-1564.
[122]Cevher V, Krause A. Greedy dictionary selection for sparse representation [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(5):979-988.
[123]Jafari M G, Plumbley M D. Fast dictionary learning for sparse representations of Speech Signals [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(5):1025-1031.
[124]Engan K, Skretting K, Husoy J H. Family of iterative LS-based dictionary learning algorithms [J]. ILS-DLA, for sparse signal representation Original Research Article,2007,17(1):32-49.
[125]Yang S, Liu Z, Wang M, et.al. Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction [J]. Neurocomputing,2011,74(17):3193-3203.
[126]Zhang S, Zhan Y, Metaxas D N. Deformable segmentation via sparse representation and dictionary learning [J]. Medical Image Analysis, In Press, Corrected Proof,2012
[127]Yang C, Shen J, Peng J, et,al. Image collection summarization via dictionary learning for sparse representation [J]. Pattern Recognition,2012.
[128]Khaninezhad M M, Jafarpour B, Li L. Sparse geologic dictionaries for subsurface flow model calibration [J]. Advances in Water Resources.2012,39:106-121.
[129]Xu P, Yao D. Development and evaluation of the sparse decomposition method with mixed overcomplete dictionary for evoked potential estimation [J]. Computers in Biology and Medicine,2007, 37(12):1731-1740.
[130]Xie Z, Feng J. KFCE:A dictionary generation algorithm for sparse representation[J]. Signal Processing.2009,89(10):2072-2077.
[131]Mairal J, Sapiro G. Elad M. Multiscale sparse image representationwith learned dictionaries [C|. IEEE International Conference on Image Processing,2007,3:111-105-111-108
[132]Faktor T. Denoising of image patches via sparse representations with learned statistical dependencies [C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011:5820-5823
[133]Georgiev P, Theis F, Cichocki A. Sparse component analysis and blind source separation of underdetermined mixtures [J]. IEEE Transactions on Neural Networks,2005,16(4):992-996.
[134]Bobin J, Starck J L, Fadili J M, et.al. Morphological component analysis:an adaptive thresholding strategy [J]. IEEE Transactions on Image Processing,2007,16(11):2675-2681.
[135]Bobin J, Moudden Y, Starck J L, et.al. Morphological diversity and source separation [J]. IEEE Signal Processing Letters,2006,13(7):409-412.
[136]Peng D, Zhang Y. Convergence analysis of a deterministic discrete time system of feng's MCA learning algorithm [J]. IEEE Transactions on Signal Processing,2006,54(9):3626-3632.
[137]Dubois S, Peteri R., Menard M. Decomposition of dynamic textures using morphological component analysis:a new adaptative strategy [C].20th International Conference on Pattern Recognition (ICPR), 2010:2258-2261.
[138]Gao X, Wang Y, Li X, et.al. On combining morphological component analysis and concentric morphology model for mammographic mass detection [J]. IEEE Transactions on Information Technology in Biomedicine,2010,14(2):266-273.
[139]Morris H, De P. Morphological component analysis and STAP filters [C]. Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers,2007:2187-2190
[140]Abolghasemi V, Ferdowsi S, Sanei S. Blind separation of image sources via adaptive dictionary learning [J]. IEEE Transactions on Image Processing,2012,21(6):2921-2930.
[141]Yang J, Wright J, Huang T, et.al. Image super-resolution as sparse representation of raw image patches [J]. IEEE Conference on Computer Vision and Pattern Recognition,2008:1-4.
[142]Dong W, Zhang L, Shi G, et,al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization [J]. IEEE Transactions on Image Processing,2011,20(7):1838-1857.
[143]Wright J, Yang A Y, Ganesh A, et.al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[144]Zhang L, Wang L, Lin W. Conjunctive patches subspace learning with side information for collaborative image retrieval [J]. IEEE Transactions on Image Processing,2012,21(8):3707-3720.
[145]Zhang Q. Discriminative K-SVD for dictionary learning in face recognition [C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2010:13-18.
[146]Aenchbacher W, Kurzweg T. Single-pixel, MEMS scanning mirror camera [C]. IEEE Photonics Conference (PHO),2011:849-850.
[147]Chen H, Xi N, Song B, et.al. Single pixel infrared camera using a carbon nanotube photodetector [J]. IEEE Sensors,2011:1362-1366.
[148]Liu L, Wang A, Li Z, et.al. An improved distributed compressive video sensing based on adaptive sparse basis [C]. International Conference on Robot, Vision and Signal Processing (RVSP),2011:137-140.
[149]Diana Rexiline D N, Anusmina D J. Fusion and restoration of multifocus image using sparse representation [C]. International Conference on Advances in Engineering, Science and Management (ICAESM),2012:291-296.
[150]Drajic D, Cvejic N. Multimodal image fusion in presence of noise using dparse coding of ICA coefficients [C]. IEEE International Symposium on Signal Processing and Information Technology, 2007:343-346
[151]Zhu X X, Wang X, Bamler R. Compressive sensing for image fusion-with application to pan-sharpening [C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2011: 2793-2796
[152]Divekar A, Ersoy O. Image fusion by compressive sensing [C]. International Conference on Geoinformatics,2009:1-6
[153]Needell D, Vershynin R. Signal recovery from inaccurate and incomplete measurements via regularized orthogonal matching pursuit [J]. IEEE Journal of Selected Topics in Signal Processing, 2010,4:310-316.
[154]Giryes R, Elad M. RIP-based near-oracle performance guarantees for SP, CoSaMP, and IHT [J]. IEEE Transactions on Signal Processing,2012,60(3):1465-1468.
[155]Donoho D L, Tsaig Y, Drori I, et.al. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit [J]. IEEE Transactions on Information Theory,2012,58(2): 1094-1121.
[156]Lin C H, Yuan S A, Chiu S W, et.al. ProgressFace:an algorithm to improve routing efficiency of GPSR-like routing protocols in wireless Ad Hoc networks [J]. IEEE Transactions on Computers,2010, 59(6):822-834.
[157]Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problem [J]. SIAM J.IMAGI NGSCIENCES,2009,2(2):183-202.
[158]Li T Y, Rhee N H. Homotopy algorithm for symmetric eigenvalue problems [J]. Numerische Mathematik,1989,55(3):265-280.
[159]Andreas W, Lorenz T B. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming [J]. Mathematical Programming,2006,106(1):25-57.
[160]Guo P, Wang X, Han Y. The enhanced genetic algorithms for the optimization design [C]. International Conference on Biomedical Engineering and Informatics (BMEI),2010,7:2990-2994.
[161]高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用,2004,1：47-50.
[162]张纪会.自适应蚁群算法[J].控制理论与应用,2000,17(1)：1-8.
[163]Lustig M, Donoho D, Pauly J M. Sparse mri:The application of compressed sensing for rapid mr imaging [J]. Magnetic Resonance in Medicine,2007,58(6):1182-1195
[164]练秋生,郝鹏鹏.基于压缩感知和代数重建的CT图像重建[J].光学技术.2009,35(3)：423-425.
[165]Jung H, Sung K, Nayak K S, et al. K-t FOCUSS:A general compressed sensing framework for high Resolution Dynamic MRI [J]. Magnetic Resonance in Medicine,2007,35(6):2313-2351.
[166]Cvejic N, Canagarajah C N, Bull D R. Image fusion metric based on mutual information and Tsallis entropy [J]. Electronics Letters,2006,42(1 I):1-2.
[167]Pajares G. A wavelet-based image fusion tutorial [J]. Pattern Recognition,2004,37(9):1855-1872.
[168]Nikolaos M, Tania S. Optimal contrast correction for ICA-based fusion of multimodal images [J]. IEEE Sensors Journal,2008,8(12):2016-2025.
[169]Nikolov S. The image fusion server [EB/OL].2005[2008,02]. http://www.imagefusion.org/
[170]Lian Q, Gao Y. Sparse MRI reconstruction via different norms based on total variation [C]. International Conference on Audio, Language and Image Processing,2008:7-9.
[171]曹杰,龚声蓉,刘纯平.一种新的基于小波变换的多聚焦图像融合算法[J].计算机工程与应用,2007,43(24)：47-50.
[172]Drajic D, Cvejic N. Adaptive fusion of multimodal surveillance image sequences in visual sensor networks [J]. IEEE Transactions on Consumer Electronics,2007,53(4):1456-1462.
[173]Nikolaos M, Tania S. Optimal contrast correction for ICA-based fusion of multimodal images [J]. IEEE Sensors Journal,2008,8(12):2016-2025.
[174]Luo X, Zhang J, Yang J, et.al. Image fusion in compressed sensing [C]. IEEE Int. Conf. Image Process,2009:2205-2208.
[175]Wan T, Canagarajah N, Achim A. Compressive image fusion [C]. IEEE Int. Conf. Image Process, 2008:1308-1311.
[176]Grace Chang S, Yu B, Vetterli M. Wavelet thresholding for multiple noisy image copies [J]. IEEE Trans. Image Process.2000,9(9):1631-1635.
[177]Luisier F, Blu T, Unser M. SURE-LET for orthonormal wavelet-domain video denoising [J]. IEEE Trans. Circuits Syst. Video Technol,2010,20 (6):913-919.
[178]Banerjee S. Low-power content-based video acquisition for super-resolution enhancement [J]. IEEE Trans. Multimed.2009,11 (3):455-464.
[179]Popovici A, Dan P. Cellular automata in image processing [C]. In:Proceedings of AMS,2000:1-6.
[180]Ji Z, Liao H, Zhang X, et.al. Simple and efficient soft morphological filter in periodic noise reduction [C]. IEEE Region 10 Conference,2006:1-4.
[181]Fisher R. CAVIAR test case scenarios [EB/OL]. (2003,07,11) [2007]. http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATAl [EB/OL].
[2]Yu H, Zhao L, Wang H. Image denoising using trivariate shrinkage filter in the Wavelet domain and joint bilateral filter in the spatial domain [J]. IEEE Transactions on Image Processing,2009,18(10): 2364-2369.
[3]Zhang L, Li X, Zhang D. Image denoising and zooming under the linear minimum mean square-error estimation framework [J]. IET Image Processing,2012,6(3):273-283.
[4]Kasprzak W, Cichocki A. Hidden image separation from incomplete image mixtures by independent component analysis [J]. Proceedings of the 13th International Conference on Pattern Recognition,1996, 2:394-398.
[5]Tonazzini A, Bedini L, Salerno E. A Markov model for blind image separation by a mean-field EM algorithm [J]. IEEE Transactions on Image Processing,2006,15(2):473-482.
[6]Be'ery E, Yeredor A. Blind separation of superimposed shifted images using parameterized joint diagonalization [J]. IEEE Transactions on Image Processing,2008,17(3):340-353.
[7]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature,1996,381(6583):607-609.
[8]Vinje W E, Gallant J L. Sparse coding and decorrelation in primary visual cortex during natural vision [J]. Science,2000,287:1273-1276.
[9]Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition [C]. Asilomar Conf. Signals, Syst. Comput., 1993,1.
[10]Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit [J]. SI AM Rev.,2001, 43(1):129-159.
[11]Harmany Z T, Marcia R F, Willett R M. This is SPIRAL-TAP:Sparse Poisson Intensity Reconstruction ALgorithms-Theory and Practice [J]. IEEE Transactions on Image Processing,2010, 21(3):1084-1096.
[12]Candes E, Romberg J, Tao T. Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information [J]. IEEE Trans. on Information Theory,2006,52(2):489-509.
[13]Candes E. Compressive Sampling [J]. Int. Congress of Mathematics,2006,3:1433-1452.
[14]Baraniuk R. Compressive sensing [J]. IEEE Signal Processing Magazine,2007,24(4):118-121.
[15]Candes E, Wakin M. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008,25(2):21-30.
[16]Liu Z, Blasch E, Xue Z, et.al. Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision:A comparative study[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(1):94-109.
[17]Youshen X, Kamel M S. Novel cooperative neural fusion algorithms for image restoration and image fusion [J]. IEEE Transactions on Image Processing,2007.16(2):367-381.
[18]Xu M, Chen H, Varshney P K. An image fusion approach based on markov random fields [J]. IEEE Transactions on Geoscience and Remote Sensing,2011,49(12):5116-5127.
[19]Matsopoulos G K, Marshall S, Brunt J N H. Multiresolution morphological fusion of MR and CT images of the human brain Vision [J], IEE Proceedings Image and Signal Processing,1994,141(3): 137-142.
[20]Zhang Q, Guo B. Infrared and Color Visible Images Fusion Based on Second Generation Curvelet Transform [J]. Industrial Electronics and Applications,2007:2118-2123.
[21]Kong W, Lei Y, Ni X. Fusion technique for grey-scale visible light and infrared images based on non-subsampled contourlet transform and intensity-hue-saturation transform [J]. IET Signal Processing,2011,5(1):75-80.
[22]Agrawal D, Singhai J. Multifocus image fusion using modified pulse coupled neural network for improved image quality [J]. IET Image Processing,2010,4(6):443-451.
[23]Wang W W, Shui P L, Song G X. Multifocus image fusion in wavelet domain Machine [C]. International Conference on Learning and Cybernetics,2003,5:2887-2890.
[24]Xue X, Lei G, Wang H, et. al. A parallel fusion method of remote sensing image based on IHS transformation [C]. International Congress on Image and Signal Processing (CISP),2011,3: 1600-1603.
[25]Farah I R, Boulila W, Ettabaa K S, et.al. Interpretation of multisensor remote sensing images: multiapproach fusion of uncertain information [J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(12):4142-4152.
[26]Kempeneers P, Sedano F, Seebach L, et.al. Data fusion of different spatial resolution remote sensing images applied to forest-type mapping [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(12):4977-4986.
[27]Rehman A, Wang Z, Brunei D, et.al. SSIM-inspired image denoising using sparse representations Acoustics [C]. IEEE International Conference on Speech and Signal Processing (ICASSP),2011: 1121-1124.
[28]Gilboa G, Sochen N, Zeevi Y Y. Variational denoising of partly textured images by spatially varying constraints [J]. IEEE Transactions on Image Processing,2006,15(8):2281-2289.
[29]Kivanc M M, Kozintsev I, Ramchandran K, et.al. Low-complexity image denoising based on statistical modeling of wavelet coefficients [J]. IEEE Signal Processing Letters,1999,6(12):300-303.
[30]Zheng Y, Fu H, Au O K C, et.al. Bilateral normal filtering for mesh denoising [J]. IEEE Transactions on Visualization and Computer Graphics,2011,17(10):1521-1530.
[31]Duarte-Carvajalino J M, Sapiro G, Velez-Reyes M, et.al. Multiscale representation and segmentation of hyperspectral imagery using geometric partial differential equations and algebraic multigrid methods [J]. IEEE Transactions on Geoscience and Remote Sensing,2008,46(8):2418-2434.
[32]Liu W, Ma Z. Wavelet Image threshold denoising based on edge detection [C]. IMACS Multiconference on Computational Engineering in Systems Applications,2006,1:72-78.
[33]Ghazel M, Freeman G H, Vrscay E R. Fractal-wavelet image denoising [J]. International Conference on Image Processing,2002,1:1-836-1-839.
[34]Shui P L, Zhou Z F, Li J X. Image denoising algorithm via best wavelet packet base using Wiener cost function [J]. IET Image Processing.2007,1(3):311-318.
[35]Lu J, Zou Y, Ye Z. Enhanced fractal-wavelet image denoising [C]. ISECS International Colloquium on Computing, Communication, Control, and Management,2008,1:115-119.
[36]Shidahara M, Ikoma Y, Seki C, et.al. Wavelet denoising of dynamic PET data:Application to the parametric imaging of peripheral benzodiazepine receptor [C]. IEEE Nuclear Science Symposium Conference Record,2006,1:3217-3220.
[37]Yu H, Zhao L, Wang H. Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain [J]. IEEE Transactions on Image Processing,2009,18(10): 2364-2369.
[38]Chang S G, Bin Y, Vetterli M. Multiple copy image denoising via wavelet thresholding [C]. International Conference on Image Processing,1998,1:545-549.
[39]Liang T, Zheng Z, Lei S, et.al. Laplace prior based distributed compressive sensing [C]. Internationa! ICST Communications and Networking in China (CHINACOM),2010:1-4.
[40]Zeng F, Chen L, Zhi T. Distributed compressive spectrum sensing in cooperative multihop cognitive networks [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(1):37-48.
[41]Aslantas V, Kurban R. Fusion of multi-focus images using differential evolution algorithm [J]. Expert Systems with Applications,2010,37(12):8861-8870.
[42]Maruthi R. Spatial domain method for fusing multi-focus images using measure of fuzziness [J]. International Journal of Computer Applications,2011,20(7):48-51.
[43]Oudre L, Stathaki T, Mitianoudis N. Image fusion using optimization of statistical measurements [M]. Book chapter. Image fusion:Algorithms and Applications, Academic Press.2008:251-272.
[44]Li S, Kwok J T, Wang Y. Combination of images with diverse focuses using the spatial frequency [J]. Information Fusion,2001,2(3):169-176.
[45]Zhang Y, Ge L. Efficient fusion scheme for multi-focus images by using blurring measure [J]. Digital Signal Processing,2009,19(2):186-193.
[46]Petrovic V S, Xydeas C S. Gradient-dased multiresolution image fusion [J]. IEEE Transactions on image processing,2004,13(2):228-237.
[47]Ren H, Lan Y, Zhang Y. Research of multi-focus image fusion based on M-band multi-wavelet transformation [C]. Advanced Computational Intelligence (IWACI),2011:395-398.
[48]Ye Z, Mohamadian H, Ye Y. Sensing data discrete wavelet fusion for pattern recognition with qualitative and quantitative measuring [J]. Neural Networks,2008:3647-3652.
[49]Ma J, Gong M, Zhou Z. Wavelet fusion on ratio images for change detection in SAR images [J]. IEEE Geoscience and Remote Sensing Letters,2012,9(6):1122-1126.
[50]Ranchin T, Wald L. Data fusion of remotely sensed images using the wavelet transform:the ARSIS solution [C]. Proceedings, SPIE Annual Meeting, International Symposium on Optical Science, Engineering and Instrumentation,1997,3169:272-280.
[51]Chipman L J, Orr T M, Graham L N. Wavelets and image fusion [C]. International Conference on Image Processing, Washington,1995:248-251.
[52]Prakash N K. Image fusion algorithm based on biorthogonal wavelet [J]. International Journal of Enterprise Computing and Business System International Systems,2011,1(2)
[53]Roshni V S. Mutual information based registration and region based wavelet fusion of images [J]. Computer Vision, Graphics & Image Processing,2008:606-613
[54]Kim Y. Improved Additive-wavelet image fusion [J]. IEEE Geoscience and Remote Sensing Letters, 2011,8(2):263-267.
[55]曹玉珍,刘晓婷等.基于凸集投影与小波融合的图像超分辨率重建方法[J].生物医学工程学杂志,2009,26(5)：3947-951.
[56]周礼,王章野,金剑秋等.基于HVS的小波图像融合新算法[J].中国图像图形学报,2004,9(9)：1088-1094.
[57]Zhang H. A novel medical image fusion method [C]. International Conference on Multimedia and Ubiquitous Engineering,2007:548-553
[58]Cai X. Wavelet image fusion based on the high order polynomial regression [C]. IEEE International Geoscience and Remote Sensing Symposium,2007:3100-3103
[59]Omar Z, Mitianoudis N, Stathaki T. Region-based image fusion using a combinatory Chebyshev-ICA method [C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011:1213-1216
[60]Hyvarinen A, Hoyer P, Inki M. Topographic ICA as a model of V1 receptive fields [C]. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks,2000,4:83-88.
[61]Mitianoudis N, Stathaki T. Pixel-based and region-based image fusion schemes using ICA bases [J]. Information Fusion,2007,8(2):131-142.
[62]Mitianoudis N, Stathaki T. A daptive image fusion using ICA bases [C]. IEEE International Conference on Acoustics, Speech and Signal Processing,2006:1-4
[63]Yang B, Li S. Multifocus Image fusion and restoration with sparse representation [J]. IEEE Trans. Instrumentation and Measurement,2010,59(4):884-891.
[64]Hu J, Li S, Yang B. Remote sensing image fusion based on IHS transform and sparse representation [C]. Chinese Conference on Pattern Recognition (CCPR),2010:1-4.
[65]Yang B, Li S. Pixel-level image fusion with simultaneous orthogonal matching pursuit [J]. Information Fusion,2012:10-19
[66]Tropp J A, Gilbert A C, Strauss M J. Algorithms for simultaneous sparse approximation [J]. Signal Processing,2006,86 (3):572-588.
[67]Wang Q, Shen Y, Jin J. Performance evaluation of image fusion techniques [J]. Image Fusion: Algorithms and Applications, ch.19, T. Stathaki, ed., pp. Elsevier,2008:469-492.
[68]Hossny M, Nahavandi S, Vreighton D. Comments on "Information Measure for Performance of Image Fusion" [J]. Electronics Letters,2008,44(18):1066-1067.
[69]Xydeas C, Petrovic V. Objective image fusion performance measure [J]. Electronic Letter,2000,6(4): 308-309.
[70]Piella G, Heijmans H. A new quality metric for image fusion [C]. Image Processing. Spain:IEEE, 2003:173-176.
[71]Cvejic N, Loza A. Bull D, et.al. A similarity metric for assessment of image fusion algorithms [J]. International Journal of Signal Processing,2005.2(3):178-182.
[72]胡浩,王明照.杨杰自适应模糊加权均值滤波器[J].系统工程与电子技术,2002,24(2)：15-19.
[73]柴宝仁.中值滤波在气象传真图中降下噪的分析[J].北京理工大学学报,2011,1(31)：417-419
[74]胡小平,陈国良,毛征宇等.离焦模糊图像的维纳滤波复原研究[J].仪器仪表学报,2007,28(3)：479-483.
[75]聂坚,郑金华,谢泞志等.傅里叶空间变换处理带噪声进化算法的研究[J].计算机工程与应用,2011,47(28)：33-36
[76]Xia R, MENG K, QIAN F, et.al. Online wavelet denoising via a moving window original research Article [J]. Acta Automatica Sinica,2007,33(9):897-901.
[77]Park H, Lee T. Capturing nonlinear dependencies in natural images using ICA and mixture of Laplacian distribution [J]. Neurocomputing,2006,69(13-15):1513-1528.
[78]Aharon M, Elad M, Bruckstein A. K-SVD:An algorithm for designing overcomplete dictionaries for sparse representation [J]. IEEE Trans. Signal Process.,2006,54(11):4311^4322.
[79]Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries [J]. IEEE Trans, Image Process.,2006,15(12):3736-3745.
[80]Laska J N. Exact signal recovery from sparsely corrupted measurements through the Pursuit of Justice [J]. Signals, Systems and Computers,2009:1556-1560
[81]Chang G, Yu B, Vetterli M. Wavelet thresholding for multiple noisy image copies [J]. IEEE Transactions on Image Processing,2000,9(9):1631-1635.
[82]Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries [J]. IEEE Transactions on Signal Processing,1993,41(12):3397-3415.
[83]Duarte M, Davenport M, Takhar D, et.al. Single-pixel imaging via compressive sampling [J]. IEEE Signal Processing Magazine,2008,25(2):83-91.
[84]Chan W L, Moravec M, Baraniuk R, et.al. Terahertz imaging with compressed sensing and phase retrieval [J]. Optics Letters,2008,33:974-976.
[85]Coskun A F, Su T, Sencan I, et.al. Lensfree fluorescent on-chip Imaging using compressive sampling [J]. Optics & Photonics News,2010,21(12):27-27.
[86]Pati Y C. Orthogonal matching pursuit:recursive function approximation with applications to wavelet decomposition [C]. Proceedings of the 27 th Annual Asilomar Conference on Signals, Systems, and Computers,1993,1:40-44.
[87]Candes E, Michael W. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine,2008,25(2):21-30.
[88]Romberg J. Imaging via compressive sampling [J]. IEEE Signal Processing Magazine,2008,25(2):14-20.
[89]Angelosante D, Giannakis G B. RLS-weighted Lasso for adaptive estimation of sparse signals [C]. IEEE International Conference on Acoustics. Speech and Signal Processing,2009:3245-3248.
[90]He Z, He Z, Cichocki A, et.al. Improved FOCUSS method with conjugate gradient iterations [J]. IEEE Transactions on Signal Processing,2009,57(1):399-404.
[91]Chartrand R. Exact reconstructions of sparse signals via nonconvex minimization[J]. IEEE Signal Proc. Lett.,2007,14(10):707-710.
[92]Mairal J, Elad M, Sapiro G. Sparse representation for color image restoration [J]. IEEE Transactions on Image Processing,2008,17(1):53-69.
[93]Yang J, Wright J, Huang T S, et.al. Image super-resolution via sparse representation [J]. IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[94]Panagakis Y, Kotropoulos C. Music genre classification via topology preserving non-negative tensor factorization and sparse representations [C]. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010:249-252.
[95]Ji Y, Lin T, Zha H. Mahalanobis distance based non-negative sparse representation for face recognition [C]. International Conference on Machine Learning and Applications,2009:41-46.
[96]Kumar M, Loui A C. Key frame extraction from consumer videos using sparse representation [C]. IEEE International Conference on Image Processing (ICIP),2011:2437-2440
[97]Drajic D, Cvejic N. Multimodal image fusion in presence of noise using sparse coding of ICA [C]. IEEE International Symposium on Signal Processing and Information Technology,2007:343-346.
[98]李志清,施智平,李志欣等.结构相似度稀疏编码及其图像特征提取[J],模式识别与人工智能,2010,23(1)：17-22.
[99]Nirenberg S, Carcieri S M, Jacobs A L, et.al. Retinal ganglion cells act largely as independent encoders [J]. Nature,2001,411:698-701.
[100]邓承志.图像稀疏表示理论及其应用研究[D].华中科技大学,博士学位论文.
[101]Donoho D, Huo X. Beamlets and multiscale image analysis [J]. Springer Lecture Notes in Computational Science and Engineering,2002,20:149-196.
[102]Bruckstein A M, Elad M, Zibulevsky M. Sparse non-negative solution of a linear system of equations is unique [C].3rd International Symposium on Communications, Control and Signal Processing,2008: 762-767.
[103]Ben-Haim Z, Eldar Y C. Near-oracle performance of greedy block-sparse estimation techniques from noisy measurements [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(5):1032-1047.
[104]Duarte M, Sarvotham S, Baron D, et.al. Distributed compressed sensing of jointly sparse signals [J]. in Asilomar Conf. Signals, Systems and Computers, (Pacific Grove, CA),2005:1-4.
[105]Baron D, Duarte M, Wakin M, et.al. Distributed compressive sensing [C]. in Proceedings of the Sensor, Signal and Information Processing Workshop,2008:1-4.
[106]曾绍华.LS-SVM的组合优化算法研究[J].计算机工程与应用,2007,43(22)：89-92.
[107]陈发宇,尚永生,杨长春.Matching Pursuits方法综述[J].地球物理学进展,2007,22(5)：1466-1473.
[108]Padberg M, Rinaldi G. A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems [J], Industrial and Applied Mathematics,1991,33(1):60-100
[109]Qiu Q, Jiang Z, Chellappa R. Sparse dictionary-based representation and recognition of action attributes [C]. IEEE International Conference on Computer Vision (ICCV).2011:707-714.
[110]Lee K, Tak S, Ye J C. A data-driven dparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion [J]. IEEE Transactions on Medical Imaging,2011,30(5):1076-1089.
[111]Yang C, Peng J, Fan J. Image collection summarization via dictionary learning for sparse representation [J]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2012: 1122-1129.
[112]Horev I, Bryt O, Rubinstein R. Adaptive image compression using sparse dictionaries [C].19th International Conference on Systems, Signals and Image Processing (IWSSIP),2012:592-595.
[113]Jafari M G, Plumbley M D, Davies M E. Speech separation using an adaptive sparse dictionary algorithm [C]. Hands-Free Speech Communication and Microphone Arrays,2008:25-28
[114]Jajamovich G H, Wang X. Haplotype inference based on sparse dictionary selection [C]. Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011:1021-1025.
[115]Lee K., Ye J C. Statistical parametric mapping of FMR1 data using sparse dictionary learning [C]. IEEE International Symposium on Biomedical Imaging:From Nano to Macro,2010:660-663.
[116]Eavani H, Filipovych R, Davatzikos C, et.al Sparse dictionary learning of resting state fMRI networks [C]. International Workshop on Pattern Recognition in NeuroImaging (PRNI),2012:73-76.
[117]Jolliffe I T. Principal component analysis [C]. Springer, New York:2002:1-4.
[118]Engan K, Aase S O, Husoy J H. Multi-frame compression:theory and design [J]. EURASIP Signal Process.,2000,80(10):2121-2140.
[119]Engan K, Skretting K, Husoy J H. Denoising of images using designed signal dependent frames and matching pursuit [C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005,2:653-656.
[120]Tron R, Vidal R. Distributed computer vision algorithms through distributed averaging [C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2011:57-63.
[121]Rubinstein R, Zibulevsky M, Elad M. Double sparsity:learning sparse dictionaries for sparse signal approximation [J]. IEEE Transactions on Signal Processing,2010,58(3):1553-1564.
[122]Cevher V, Krause A. Greedy dictionary selection for sparse representation [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(5):979-988.
[123]Jafari M G, Plumbley M D. Fast dictionary learning for sparse representations of Speech Signals [J]. IEEE Journal of Selected Topics in Signal Processing,2011,5(5):1025-1031.
[124]Engan K, Skretting K, Husoy J H. Family of iterative LS-based dictionary learning algorithms [J]. ILS-DLA, for sparse signal representation Original Research Article,2007,17(1):32-49.
[125]Yang S, Liu Z, Wang M, et.al. Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction [J]. Neurocomputing,2011,74(17):3193-3203.
[126]Zhang S, Zhan Y, Metaxas D N. Deformable segmentation via sparse representation and dictionary learning [J]. Medical Image Analysis, In Press, Corrected Proof,2012
[127]Yang C, Shen J, Peng J, et,al. Image collection summarization via dictionary learning for sparse representation [J]. Pattern Recognition,2012.
[128]Khaninezhad M M, Jafarpour B, Li L. Sparse geologic dictionaries for subsurface flow model calibration [J]. Advances in Water Resources.2012,39:106-121.
[129]Xu P, Yao D. Development and evaluation of the sparse decomposition method with mixed overcomplete dictionary for evoked potential estimation [J]. Computers in Biology and Medicine,2007, 37(12):1731-1740.
[130]Xie Z, Feng J. KFCE:A dictionary generation algorithm for sparse representation[J]. Signal Processing.2009,89(10):2072-2077.
[131]Mairal J, Sapiro G. Elad M. Multiscale sparse image representationwith learned dictionaries [C|. IEEE International Conference on Image Processing,2007,3:111-105-111-108
[132]Faktor T. Denoising of image patches via sparse representations with learned statistical dependencies [C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011:5820-5823
[133]Georgiev P, Theis F, Cichocki A. Sparse component analysis and blind source separation of underdetermined mixtures [J]. IEEE Transactions on Neural Networks,2005,16(4):992-996.
[134]Bobin J, Starck J L, Fadili J M, et.al. Morphological component analysis:an adaptive thresholding strategy [J]. IEEE Transactions on Image Processing,2007,16(11):2675-2681.
[135]Bobin J, Moudden Y, Starck J L, et.al. Morphological diversity and source separation [J]. IEEE Signal Processing Letters,2006,13(7):409-412.
[136]Peng D, Zhang Y. Convergence analysis of a deterministic discrete time system of feng's MCA learning algorithm [J]. IEEE Transactions on Signal Processing,2006,54(9):3626-3632.
[137]Dubois S, Peteri R., Menard M. Decomposition of dynamic textures using morphological component analysis:a new adaptative strategy [C].20th International Conference on Pattern Recognition (ICPR), 2010:2258-2261.
[138]Gao X, Wang Y, Li X, et.al. On combining morphological component analysis and concentric morphology model for mammographic mass detection [J]. IEEE Transactions on Information Technology in Biomedicine,2010,14(2):266-273.
[139]Morris H, De P. Morphological component analysis and STAP filters [C]. Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers,2007:2187-2190
[140]Abolghasemi V, Ferdowsi S, Sanei S. Blind separation of image sources via adaptive dictionary learning [J]. IEEE Transactions on Image Processing,2012,21(6):2921-2930.
[141]Yang J, Wright J, Huang T, et.al. Image super-resolution as sparse representation of raw image patches [J]. IEEE Conference on Computer Vision and Pattern Recognition,2008:1-4.
[142]Dong W, Zhang L, Shi G, et,al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization [J]. IEEE Transactions on Image Processing,2011,20(7):1838-1857.
[143]Wright J, Yang A Y, Ganesh A, et.al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[144]Zhang L, Wang L, Lin W. Conjunctive patches subspace learning with side information for collaborative image retrieval [J]. IEEE Transactions on Image Processing,2012,21(8):3707-3720.
[145]Zhang Q. Discriminative K-SVD for dictionary learning in face recognition [C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2010:13-18.
[146]Aenchbacher W, Kurzweg T. Single-pixel, MEMS scanning mirror camera [C]. IEEE Photonics Conference (PHO),2011:849-850.
[147]Chen H, Xi N, Song B, et.al. Single pixel infrared camera using a carbon nanotube photodetector [J]. IEEE Sensors,2011:1362-1366.
[148]Liu L, Wang A, Li Z, et.al. An improved distributed compressive video sensing based on adaptive sparse basis [C]. International Conference on Robot, Vision and Signal Processing (RVSP),2011:137-140.
[149]Diana Rexiline D N, Anusmina D J. Fusion and restoration of multifocus image using sparse representation [C]. International Conference on Advances in Engineering, Science and Management (ICAESM),2012:291-296.
[150]Drajic D, Cvejic N. Multimodal image fusion in presence of noise using dparse coding of ICA coefficients [C]. IEEE International Symposium on Signal Processing and Information Technology, 2007:343-346
[151]Zhu X X, Wang X, Bamler R. Compressive sensing for image fusion-with application to pan-sharpening [C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2011: 2793-2796
[152]Divekar A, Ersoy O. Image fusion by compressive sensing [C]. International Conference on Geoinformatics,2009:1-6
[153]Needell D, Vershynin R. Signal recovery from inaccurate and incomplete measurements via regularized orthogonal matching pursuit [J]. IEEE Journal of Selected Topics in Signal Processing, 2010,4:310-316.
[154]Giryes R, Elad M. RIP-based near-oracle performance guarantees for SP, CoSaMP, and IHT [J]. IEEE Transactions on Signal Processing,2012,60(3):1465-1468.
[155]Donoho D L, Tsaig Y, Drori I, et.al. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit [J]. IEEE Transactions on Information Theory,2012,58(2): 1094-1121.
[156]Lin C H, Yuan S A, Chiu S W, et.al. ProgressFace:an algorithm to improve routing efficiency of GPSR-like routing protocols in wireless Ad Hoc networks [J]. IEEE Transactions on Computers,2010, 59(6):822-834.
[157]Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problem [J]. SIAM J.IMAGI NGSCIENCES,2009,2(2):183-202.
[158]Li T Y, Rhee N H. Homotopy algorithm for symmetric eigenvalue problems [J]. Numerische Mathematik,1989,55(3):265-280.
[159]Andreas W, Lorenz T B. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming [J]. Mathematical Programming,2006,106(1):25-57.
[160]Guo P, Wang X, Han Y. The enhanced genetic algorithms for the optimization design [C]. International Conference on Biomedical Engineering and Informatics (BMEI),2010,7:2990-2994.
[161]高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用,2004,1：47-50.
[162]张纪会.自适应蚁群算法[J].控制理论与应用,2000,17(1)：1-8.
[163]Lustig M, Donoho D, Pauly J M. Sparse mri:The application of compressed sensing for rapid mr imaging [J]. Magnetic Resonance in Medicine,2007,58(6):1182-1195
[164]练秋生,郝鹏鹏.基于压缩感知和代数重建的CT图像重建[J].光学技术.2009,35(3)：423-425.
[165]Jung H, Sung K, Nayak K S, et al. K-t FOCUSS:A general compressed sensing framework for high Resolution Dynamic MRI [J]. Magnetic Resonance in Medicine,2007,35(6):2313-2351.
[166]Cvejic N, Canagarajah C N, Bull D R. Image fusion metric based on mutual information and Tsallis entropy [J]. Electronics Letters,2006,42(1 I):1-2.
[167]Pajares G. A wavelet-based image fusion tutorial [J]. Pattern Recognition,2004,37(9):1855-1872.
[168]Nikolaos M, Tania S. Optimal contrast correction for ICA-based fusion of multimodal images [J]. IEEE Sensors Journal,2008,8(12):2016-2025.
[169]Nikolov S. The image fusion server [EB/OL].2005[2008,02]. http://www.imagefusion.org/
[170]Lian Q, Gao Y. Sparse MRI reconstruction via different norms based on total variation [C]. International Conference on Audio, Language and Image Processing,2008:7-9.
[171]曹杰,龚声蓉,刘纯平.一种新的基于小波变换的多聚焦图像融合算法[J].计算机工程与应用,2007,43(24)：47-50.
[172]Drajic D, Cvejic N. Adaptive fusion of multimodal surveillance image sequences in visual sensor networks [J]. IEEE Transactions on Consumer Electronics,2007,53(4):1456-1462.
[173]Nikolaos M, Tania S. Optimal contrast correction for ICA-based fusion of multimodal images [J]. IEEE Sensors Journal,2008,8(12):2016-2025.
[174]Luo X, Zhang J, Yang J, et.al. Image fusion in compressed sensing [C]. IEEE Int. Conf. Image Process,2009:2205-2208.
[175]Wan T, Canagarajah N, Achim A. Compressive image fusion [C]. IEEE Int. Conf. Image Process, 2008:1308-1311.
[176]Grace Chang S, Yu B, Vetterli M. Wavelet thresholding for multiple noisy image copies [J]. IEEE Trans. Image Process.2000,9(9):1631-1635.
[177]Luisier F, Blu T, Unser M. SURE-LET for orthonormal wavelet-domain video denoising [J]. IEEE Trans. Circuits Syst. Video Technol,2010,20 (6):913-919.
[178]Banerjee S. Low-power content-based video acquisition for super-resolution enhancement [J]. IEEE Trans. Multimed.2009,11 (3):455-464.
[179]Popovici A, Dan P. Cellular automata in image processing [C]. In:Proceedings of AMS,2000:1-6.
[180]Ji Z, Liao H, Zhang X, et.al. Simple and efficient soft morphological filter in periodic noise reduction [C]. IEEE Region 10 Conference,2006:1-4.
[181]Fisher R. CAVIAR test case scenarios [EB/OL]. (2003,07,11) [2007]. http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATAl [EB/OL].
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |