基于统计相关分析和视觉特性的图像信息融合方法及其应用研究
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摘要
图像信息融合能够以软件手段把对同一目标或场景的不同图像,综合成对同一目标或场景的全面、准确的描述,它在医学、遥感、军事等领域有着较为广泛的应用。良好的图像融合方法能够为后续的计算机自动化处理奠定坚实的基础。本文分析了当前图像融合方法的研究现状,深入研究了各类图像融合方法的理论,以提升图像质量及有利于后续应用为最终目标,针对噪声图像融合、图像质量评价参数对图像融合策略的作用、多光谱和多时相遥感图像融合以及基于学习的融合权重构造等开展相关的研究。论文主要研究工作及成果如下：
(1)针对噪声图像的融合,提出了结合多尺度框架和总变差模型的融合框架。该多尺度分解框架中,采用主成分分析方法进行多尺度分解与重构。一种变换要能够运用于多尺度分解框架中,必须具备正变换和逆变换的能力,主成分分析不仅可以用于分解图像并且能够较好地重构图像,因此在多尺度分解框架中的运用具有可行性。新的融合框架解决了总变差模型在图像融合过程会出现块状效应的问题,也克服了多尺度分解框架对于噪声抑制能力较差的弱点。实验表明,新框架中总变差模型的引入不仅有利于降低图像噪声,还有利于多光谱图像和全色图像融合中光谱信息的保持；基于主成分分析的多尺度分解方法的引入能够较好地避免总变差模型中出现块状效应的现象。
(2)提出了基于图像质量评价参数的融合准则。多尺度分解过程中的融合准则包括近似图像融合准则和细节图像融合准则。通常各分解层上图像进行融合时,融合准则均根据当前层图像的相关信息进行融合,研究图像分解过程可以发现,随着分解层次的增加,图像信息量逐渐减少,这种现象在近似图像中更为明显。因此,本文建立了运用多尺度变换中的上层信息来完成当前层近似图像信息融合的策略,将对应图像的质量评价参数分别用于建立近似图像和细节图像的融合准则。在多尺度融合框架下的实验表明,该融合准则能有效地提取近似图像和细节图像信息。
(3)提出针对多光谱和多时相遥感图像的基于多重集典型相关分析(MCCA)的近似图像融合准则。MCCA可以提取多组对象之间的相似信息,用于近似图像的融合,能够在剔除相关性较小信息的同时,提高融合图像的质量。该方法首先将遥感图像进行小波变换获取近似图像,然后针对近似图像采用基于MCCA的准则进行融合,用取大原则完成细节图像融合,最后经由逆变换实现组内图像的融合。与其他方法相比较,该方法能够同时处理多组图像,并且最终得到的融合图像与源图像的相关性更为密切。
(4)针对有训练集的情况,提出了结合核广义典型相关分析(KGCCA)与维纳滤波的方法,将其用于寻找相似图像、构造融合所需的权重函数,从而形成一种新的融合思路和方法,并应用于图像去噪。该方法首先运用提出的KGCCA对图像进行了特征抽取,并进一步与核Half典型相关分析(KHCCA)结合提出了核Half广义典型相关分析(KHGCCA)算法,为了减少核空间内数据计算量,提出了相应的快速算法；随后将抽取的特征与维纳滤波结合,构造出当前图像与训练集内图像的相似性函数,最后以该函数值作为融合的权重,对应的训练图像作为相似图像得到融合后结果。为了验证所提算法的性能,分别在人脸数据库以及手写体字符数据库上进行了相关实验,实验表明,该方法所提取的特征与其它特征相比更有利于提高识别率；在对噪声图像的实验中,图像恢复的效果则进一步证明了融合权重选取恰当；通过对算法时间消耗和性能的比较表明,在加入快速算法后,所提出的方法在提高效率的同时仍然能够获取较好的融合效果。
Image information fusion is able to combine the different images of the same target or scene into a complete and accurate description of the same target or scene with software tricks. It has a wide range of applications in the field of medicine, remote sensing and military. A good image fusion method can lay a solid foundation for the subsequenct computer automated processing. This dissertation analyzes the research status quo of image fusion, and then does a deep research on the theory of various image fusion methods. Aiming at improving the image quality and benefiting the follow-up applications, this dissertation researches into noisy image fusion, the role that image quality evaluation plays in image fusion rules, multi-spectral and multi-temporal remote sensing image fusion, and weight construction based on learning algorithm. The main contributions are summarized as follows:
(1) A fusion framework which contains multi-scale decomposition framework and Totoal Variation (TV) model is proposed for noisy image fusion. In the multi-scale decomposition framework, applying Principal Component Analysis (PCA) to accomplish the decomposition and reconstruction is presented. If one transform can be applied to multi-scale decomposition, it must have the ablity of transform and inverse transform. PCA can be applied not only in the decomposition but also to reconstruct images, so it is feasible to be used in multi-scale decomposition framework. The proposed framework solves the massive effect that occurs during the fusion process with TV model while overcomes the weakness of poor noisy suppression in the multi-scale framework. The experiments demonstrate that, with TV model, this new framework is able to reduce the noise of the fused image while maintain the spectral information during the fusion of multi-spectral image and panchromatic image; the proposed decomposition method based on PCA can avoid the massive effect appeared in the TV model fusion.
(2) Image fusion rules based on image quality assessment are proposed. Fusion rules in multi-scale decomposition contain approximation images' fusion rule and detail images' fusion rule. Usually, when fusing the images from certain decomposition level, fusion rules ultilize the relevant information of current level's images to accomplish the fusion. Obviously, as the increase of decomposition levels, the amount of information in the images gradually reduces. Therefore, it is proposed that using last level's information of multi-scale decomposition to complete the fusion of current level's approximation images. Corresponding Image quality assessment parameters are put into establishing the rules of approximation images and detail images. The experiments under different multi-scale decomposition frameworks show that the proposed rules are able to effectively extract information for fusion from approximation images and detail images.
(3) For multi-spectral and multi-temporal remote sensing images, Multi-set Canonical Correlation Analysis (MCCA) is applied to approximation image fusion's rule. MCCA is able to extract the similar information from group of objects. When applied to the fusion of approximation images, it can eliminate information with less correlation and at the same time improve the quality of fused image. In this method, wavelet transform is first used to acquire the approximation images of remote sensing images; then approximation images' fusion is executed by MCCA and 'select max' is considered as the detail images' fusion rule; after inverse transform, the fusion of images withinclass is realized. Compared with other methods, the rule based on MCCA is able to handle many groups of images at the same time and fusion images acquired from MCCA have closer correlation with source images.
(4) For training set, a method combined Kernel Generalized Canonical Correlation Analysis (KGCCA) with wiener filter and corresponding fast algorithm is presented, then it is applied to seek the similar images and construct weight for fusion. In this way, a new fusion method which can be used to image denoising is generated. In this method, the proposed KGCCA is first used to extract features from images. Furthermore, Kernel Half Generalized Canonical Correlation Analysis (KHGCCA) is presented based on Kernel Half Canonical Correlation Analysis (KHCCA). To reduce the computation cost in the kernel space and improve the efficiency of the algorithm, a proposed transform matrix is added into the original algorithm. After that, extracted features and wiener filter are used to construct the similarity function. In the end, the value of function is considered as the weight distributed to the corresponding image in the training set. With the weight, the denoising images can be acquired. In order to verify the recognition performance of extracted features with new algorithm, experiments are done in the face database as well as in the handwritten character database. The experiments show that, features extracted with new algorithm have better recognition performance than others. In the denoising experiments, the good results of restoration are further proofs of proper fusion weight. Comparisons of time consumption and performance of the algorithm indicates that the proposed fast algorithm method can improve efficiency while still obtain better fusion performance.
(1)针对噪声图像的融合,提出了结合多尺度框架和总变差模型的融合框架。该多尺度分解框架中,采用主成分分析方法进行多尺度分解与重构。一种变换要能够运用于多尺度分解框架中,必须具备正变换和逆变换的能力,主成分分析不仅可以用于分解图像并且能够较好地重构图像,因此在多尺度分解框架中的运用具有可行性。新的融合框架解决了总变差模型在图像融合过程会出现块状效应的问题,也克服了多尺度分解框架对于噪声抑制能力较差的弱点。实验表明,新框架中总变差模型的引入不仅有利于降低图像噪声,还有利于多光谱图像和全色图像融合中光谱信息的保持；基于主成分分析的多尺度分解方法的引入能够较好地避免总变差模型中出现块状效应的现象。
(2)提出了基于图像质量评价参数的融合准则。多尺度分解过程中的融合准则包括近似图像融合准则和细节图像融合准则。通常各分解层上图像进行融合时,融合准则均根据当前层图像的相关信息进行融合,研究图像分解过程可以发现,随着分解层次的增加,图像信息量逐渐减少,这种现象在近似图像中更为明显。因此,本文建立了运用多尺度变换中的上层信息来完成当前层近似图像信息融合的策略,将对应图像的质量评价参数分别用于建立近似图像和细节图像的融合准则。在多尺度融合框架下的实验表明,该融合准则能有效地提取近似图像和细节图像信息。
(3)提出针对多光谱和多时相遥感图像的基于多重集典型相关分析(MCCA)的近似图像融合准则。MCCA可以提取多组对象之间的相似信息,用于近似图像的融合,能够在剔除相关性较小信息的同时,提高融合图像的质量。该方法首先将遥感图像进行小波变换获取近似图像,然后针对近似图像采用基于MCCA的准则进行融合,用取大原则完成细节图像融合,最后经由逆变换实现组内图像的融合。与其他方法相比较,该方法能够同时处理多组图像,并且最终得到的融合图像与源图像的相关性更为密切。
(4)针对有训练集的情况,提出了结合核广义典型相关分析(KGCCA)与维纳滤波的方法,将其用于寻找相似图像、构造融合所需的权重函数,从而形成一种新的融合思路和方法,并应用于图像去噪。该方法首先运用提出的KGCCA对图像进行了特征抽取,并进一步与核Half典型相关分析(KHCCA)结合提出了核Half广义典型相关分析(KHGCCA)算法,为了减少核空间内数据计算量,提出了相应的快速算法；随后将抽取的特征与维纳滤波结合,构造出当前图像与训练集内图像的相似性函数,最后以该函数值作为融合的权重,对应的训练图像作为相似图像得到融合后结果。为了验证所提算法的性能,分别在人脸数据库以及手写体字符数据库上进行了相关实验,实验表明,该方法所提取的特征与其它特征相比更有利于提高识别率；在对噪声图像的实验中,图像恢复的效果则进一步证明了融合权重选取恰当；通过对算法时间消耗和性能的比较表明,在加入快速算法后,所提出的方法在提高效率的同时仍然能够获取较好的融合效果。
Image information fusion is able to combine the different images of the same target or scene into a complete and accurate description of the same target or scene with software tricks. It has a wide range of applications in the field of medicine, remote sensing and military. A good image fusion method can lay a solid foundation for the subsequenct computer automated processing. This dissertation analyzes the research status quo of image fusion, and then does a deep research on the theory of various image fusion methods. Aiming at improving the image quality and benefiting the follow-up applications, this dissertation researches into noisy image fusion, the role that image quality evaluation plays in image fusion rules, multi-spectral and multi-temporal remote sensing image fusion, and weight construction based on learning algorithm. The main contributions are summarized as follows:
(1) A fusion framework which contains multi-scale decomposition framework and Totoal Variation (TV) model is proposed for noisy image fusion. In the multi-scale decomposition framework, applying Principal Component Analysis (PCA) to accomplish the decomposition and reconstruction is presented. If one transform can be applied to multi-scale decomposition, it must have the ablity of transform and inverse transform. PCA can be applied not only in the decomposition but also to reconstruct images, so it is feasible to be used in multi-scale decomposition framework. The proposed framework solves the massive effect that occurs during the fusion process with TV model while overcomes the weakness of poor noisy suppression in the multi-scale framework. The experiments demonstrate that, with TV model, this new framework is able to reduce the noise of the fused image while maintain the spectral information during the fusion of multi-spectral image and panchromatic image; the proposed decomposition method based on PCA can avoid the massive effect appeared in the TV model fusion.
(2) Image fusion rules based on image quality assessment are proposed. Fusion rules in multi-scale decomposition contain approximation images' fusion rule and detail images' fusion rule. Usually, when fusing the images from certain decomposition level, fusion rules ultilize the relevant information of current level's images to accomplish the fusion. Obviously, as the increase of decomposition levels, the amount of information in the images gradually reduces. Therefore, it is proposed that using last level's information of multi-scale decomposition to complete the fusion of current level's approximation images. Corresponding Image quality assessment parameters are put into establishing the rules of approximation images and detail images. The experiments under different multi-scale decomposition frameworks show that the proposed rules are able to effectively extract information for fusion from approximation images and detail images.
(3) For multi-spectral and multi-temporal remote sensing images, Multi-set Canonical Correlation Analysis (MCCA) is applied to approximation image fusion's rule. MCCA is able to extract the similar information from group of objects. When applied to the fusion of approximation images, it can eliminate information with less correlation and at the same time improve the quality of fused image. In this method, wavelet transform is first used to acquire the approximation images of remote sensing images; then approximation images' fusion is executed by MCCA and 'select max' is considered as the detail images' fusion rule; after inverse transform, the fusion of images withinclass is realized. Compared with other methods, the rule based on MCCA is able to handle many groups of images at the same time and fusion images acquired from MCCA have closer correlation with source images.
(4) For training set, a method combined Kernel Generalized Canonical Correlation Analysis (KGCCA) with wiener filter and corresponding fast algorithm is presented, then it is applied to seek the similar images and construct weight for fusion. In this way, a new fusion method which can be used to image denoising is generated. In this method, the proposed KGCCA is first used to extract features from images. Furthermore, Kernel Half Generalized Canonical Correlation Analysis (KHGCCA) is presented based on Kernel Half Canonical Correlation Analysis (KHCCA). To reduce the computation cost in the kernel space and improve the efficiency of the algorithm, a proposed transform matrix is added into the original algorithm. After that, extracted features and wiener filter are used to construct the similarity function. In the end, the value of function is considered as the weight distributed to the corresponding image in the training set. With the weight, the denoising images can be acquired. In order to verify the recognition performance of extracted features with new algorithm, experiments are done in the face database as well as in the handwritten character database. The experiments show that, features extracted with new algorithm have better recognition performance than others. In the denoising experiments, the good results of restoration are further proofs of proper fusion weight. Comparisons of time consumption and performance of the algorithm indicates that the proposed fast algorithm method can improve efficiency while still obtain better fusion performance.
引文
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