基于Contourlet变换的全景图像处理关键技术研究
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摘要
Contourlet变换是一种新型的多尺度、多分辨率分析工具，本文对Contourlet变换理论进行较深入的研究，针对海量数据、细节丰富的复杂图像在环境监控及运动目标跟踪等应用方面的实际要求，以充分保护特征信息、节约硬件资源、提高系统实时性为目的，对图像的编码、边缘检测、分割及压缩传感重构等具体技术进行了研究，并将研究结果应用到全景图像中，取得了较好的效果。
首先，改进了基于小波的Contourlet变换的SPIHT编码方法。小波变换由于方向性不足，不能有效地表示图像的纹理和轮廓，Contourlet变换虽然可以满足方向性的问题，但是变换后冗余，直接编码效果差效率低。而基于小波的Contourlet变换是无冗余变换，同时子带分解方向灵活，但是传统的基于小波的Contourlet变换的SPIHT算法没有考虑低频子带与高频子带的关系，只是在高频子带之间寻找方向树的关系，并且需要对变换后的系数进行位置置换，这样必然会影响编码质量和效率。针对以上问题提出了构造虚拟低频的思想，通过构造虚拟低频建立低频子带与高频子带的关系，使方向树结构更高，压缩效果更好，为解决全景图像信息量大、冗余度高，不利于存储、处理和传输提供了有效方法。
其次，提出了基于非下采样Contourlet变换的双阈值边缘检测算法。非下采样Contourlet变换由于其平移不变性使得检测出的边缘定位准确，但是传统的基于非下采样Contourlet变换的边缘检测结果中存在伪边缘，这是固定阈值选取不当所致。采用双阈值对高频子带中的模极大值进行筛选，用得到的两个矩阵进行补偿链接可以减少伪边缘。由于非下采样Contourlet变换系数的结构特点，低频子带中也存在丰富的边缘信息，再用Canny算子对低频子带进行检测，有效的抑制了噪声，消除了伪边缘，充分保留了纹理特征，为图像理解、分割奠定了基础。
再次，提出了基于Contourlet域隐马尔可夫树模型结合上下文结构的图像分割算法。小波域隐马尔可夫树模型分割的图像结果容易产生方向边缘成分模糊和奇异性扩散现象，Contourlet变换可以充分捕捉图像中高维奇异性，通过Contourlet域隐马尔可夫树模型获得各尺度上的初始分割，采用自适应的上下文的尺度间融合方法，从合适的粗尺度的分割结果一直融合到最细尺度即像素级分割，得到最终的图像分割结果并获得了理想的效果。为进一步识别打下夯实基础，也可以将分割出的目标信息快速传输，实现远程实时监控。
最后，提出了基于Contourlet变换的正交匹配追踪压缩传感图像重构方法。压缩传感系统利用图像稀疏表示的先验知识，能从少量的观测值中重构原始图像，从而突破奈奎斯特定理提出的采样率限制。目前压缩传感系统通常利用只有三个方向的正交小波基表示图像，应用迭代收缩法求解对应的优化问题，该方法的缺点是收敛速度慢，并且重构图像有明显的伪吉布斯效应。Contourlet变换后的系数比小波系数更具稀疏性，可以用更少的系数重构同样质量的图像，并通过正交匹配追踪强制迭代终止提高效率。用复杂计算弥补硬件资源的短板，降低了获取全景图像对硬件资源的要求。
Contourlet transform is a new multi-scale, multi-resolution analysis tool. This paperstudied on the theory of Contourlet transform.According to the practical applicationrequirements of characteristics of data, much details in complex images in environmentalmonitoring and tracking of moving targets,In order to fully protect the feature information,saving the hardware resource and improve the real time of the system, The specific techniquessuch as Image coding,encoding of image, edge detection, segmentation and compressedsensing reconstruction are researched in depth. The paper presents several key technologiesexisting in the transform processing of panoramic images by Contourlet, and achieved goodresults.
Firstly, this paper improves the SPIHT coding method that is wavelet based Contourlettransform. Though Contourlet transform can meet the direction of the problem, the directionalwavelet transform not enough to effectively represent the image texture and contour, thetransform redundant and the low efficiency of the direct coding still exists. The wavelet basedContourlet transform is non-redundant transform, and the sub-band decomposition direction isflexibility, but the traditional wavelet-based SPIHT algorithm by Contourlet Transform doesnot consider the relationship between the low-frequency sub-band and high frequencysub-band, which only looks for the relationship among the high-frequency sub-band. As itneeds to replace the transformed coefficients, all the process will certainly affect the qualityand efficiency of the coding. To solve the above issues, this paper proposes the idea ofconstructing a virtual low-frequency, which means by constructing a relationship between thelow-frequency sub-band and high frequency sub-band with the virtual low-frequency willmake a higher structure of the direction of the tree and better compression. It provides aneffective method to solve the problem of large panoramic image information, highredundancy, the unfavorable storage, processing and transmission.
Secondly, this paper proposes dual-threshold edge detection algorithm based onnon-sampling Contourlet Transform. Because of the translational invariance makes thedetected edge location accuracy, the traditional edge detection results which based onnon-Contourlet Transform, exist pseudo-edge. This is due to the improper selection of thefixed threshold. It uses the double thresholds algorithm to filter maxima in the high-frequencysub-band, and obtains two matrices to compensate the link to reduce the pseudo-edge. As thetransform coefficients of non-sampling Contourlet the structural characteristics, there is richedge information in the low frequency sub-band, uses Canny operator to detect the lowfrequency sub-band, effectively controls the noise, and eliminates the pseudo-edge image for image understanding and segmentation.
Thirdly, this article points out the image segmentation algorithms, based on Contourletdomain Hidden Markov Tree Model in accordance with the context structure.Wavelet-domain Hidden Markov Tree Model Image segmentation results will be easier toexist the direction of the edge components prone to ambiguity and singularity of diffusion,Contourlet transform can fully capture the higher dimensional and singular image, to obtainthe initial scale the beginning of segmentation by Contourlet domain hidden Markov treemodel, to use the context of the scale adaptive fusion method, to integrate into the smallestscale, that is, pixel-level segmentation from the appropriate coarse scale segmentation results,in order to obtain the final result of image segmentation and the desired results. Lay a solidfoundation for further recognition. The goal of segment the information can also betransimitted rapidly, and the remote real-time monitor can be realized.
Finally, this article proposes reconstruction method, based on Contourlet transform, oforthogonal matching pursuit compressed sensing image. Compressed sensing system uses apriori knowledge by sparse representation of the image, to restructure the original image froma small number of observations, thus to break the limit of sample rate of the Nyquist samplingtheorem. Currently the compressed sensing systems usually use orthogonal wavelet with threedirections to get image, applies the iterative shrinkage method for solving the problem ofcorresponding optimization. The shortage of this method is slow convergence speed, and thereconstruction image has obvious pseudo-Gibbs effect. Coefficients of Contourlet transformis sparser than the wavelet coefficients, and can reconstruct with fewer coefficients of thesame quality images, and through orthogonal matching pursuit force iterative termination inorder to increase the efficiency. It uses complex calculations to make up for short-board ofhardware resources to reduce the requirements of accessing panoramic images of thehardware resource.
首先，改进了基于小波的Contourlet变换的SPIHT编码方法。小波变换由于方向性不足，不能有效地表示图像的纹理和轮廓，Contourlet变换虽然可以满足方向性的问题，但是变换后冗余，直接编码效果差效率低。而基于小波的Contourlet变换是无冗余变换，同时子带分解方向灵活，但是传统的基于小波的Contourlet变换的SPIHT算法没有考虑低频子带与高频子带的关系，只是在高频子带之间寻找方向树的关系，并且需要对变换后的系数进行位置置换，这样必然会影响编码质量和效率。针对以上问题提出了构造虚拟低频的思想，通过构造虚拟低频建立低频子带与高频子带的关系，使方向树结构更高，压缩效果更好，为解决全景图像信息量大、冗余度高，不利于存储、处理和传输提供了有效方法。
其次，提出了基于非下采样Contourlet变换的双阈值边缘检测算法。非下采样Contourlet变换由于其平移不变性使得检测出的边缘定位准确，但是传统的基于非下采样Contourlet变换的边缘检测结果中存在伪边缘，这是固定阈值选取不当所致。采用双阈值对高频子带中的模极大值进行筛选，用得到的两个矩阵进行补偿链接可以减少伪边缘。由于非下采样Contourlet变换系数的结构特点，低频子带中也存在丰富的边缘信息，再用Canny算子对低频子带进行检测，有效的抑制了噪声，消除了伪边缘，充分保留了纹理特征，为图像理解、分割奠定了基础。
再次，提出了基于Contourlet域隐马尔可夫树模型结合上下文结构的图像分割算法。小波域隐马尔可夫树模型分割的图像结果容易产生方向边缘成分模糊和奇异性扩散现象，Contourlet变换可以充分捕捉图像中高维奇异性，通过Contourlet域隐马尔可夫树模型获得各尺度上的初始分割，采用自适应的上下文的尺度间融合方法，从合适的粗尺度的分割结果一直融合到最细尺度即像素级分割，得到最终的图像分割结果并获得了理想的效果。为进一步识别打下夯实基础，也可以将分割出的目标信息快速传输，实现远程实时监控。
最后，提出了基于Contourlet变换的正交匹配追踪压缩传感图像重构方法。压缩传感系统利用图像稀疏表示的先验知识，能从少量的观测值中重构原始图像，从而突破奈奎斯特定理提出的采样率限制。目前压缩传感系统通常利用只有三个方向的正交小波基表示图像，应用迭代收缩法求解对应的优化问题，该方法的缺点是收敛速度慢，并且重构图像有明显的伪吉布斯效应。Contourlet变换后的系数比小波系数更具稀疏性，可以用更少的系数重构同样质量的图像，并通过正交匹配追踪强制迭代终止提高效率。用复杂计算弥补硬件资源的短板，降低了获取全景图像对硬件资源的要求。
Contourlet transform is a new multi-scale, multi-resolution analysis tool. This paperstudied on the theory of Contourlet transform.According to the practical applicationrequirements of characteristics of data, much details in complex images in environmentalmonitoring and tracking of moving targets,In order to fully protect the feature information,saving the hardware resource and improve the real time of the system, The specific techniquessuch as Image coding,encoding of image, edge detection, segmentation and compressedsensing reconstruction are researched in depth. The paper presents several key technologiesexisting in the transform processing of panoramic images by Contourlet, and achieved goodresults.
Firstly, this paper improves the SPIHT coding method that is wavelet based Contourlettransform. Though Contourlet transform can meet the direction of the problem, the directionalwavelet transform not enough to effectively represent the image texture and contour, thetransform redundant and the low efficiency of the direct coding still exists. The wavelet basedContourlet transform is non-redundant transform, and the sub-band decomposition direction isflexibility, but the traditional wavelet-based SPIHT algorithm by Contourlet Transform doesnot consider the relationship between the low-frequency sub-band and high frequencysub-band, which only looks for the relationship among the high-frequency sub-band. As itneeds to replace the transformed coefficients, all the process will certainly affect the qualityand efficiency of the coding. To solve the above issues, this paper proposes the idea ofconstructing a virtual low-frequency, which means by constructing a relationship between thelow-frequency sub-band and high frequency sub-band with the virtual low-frequency willmake a higher structure of the direction of the tree and better compression. It provides aneffective method to solve the problem of large panoramic image information, highredundancy, the unfavorable storage, processing and transmission.
Secondly, this paper proposes dual-threshold edge detection algorithm based onnon-sampling Contourlet Transform. Because of the translational invariance makes thedetected edge location accuracy, the traditional edge detection results which based onnon-Contourlet Transform, exist pseudo-edge. This is due to the improper selection of thefixed threshold. It uses the double thresholds algorithm to filter maxima in the high-frequencysub-band, and obtains two matrices to compensate the link to reduce the pseudo-edge. As thetransform coefficients of non-sampling Contourlet the structural characteristics, there is richedge information in the low frequency sub-band, uses Canny operator to detect the lowfrequency sub-band, effectively controls the noise, and eliminates the pseudo-edge image for image understanding and segmentation.
Thirdly, this article points out the image segmentation algorithms, based on Contourletdomain Hidden Markov Tree Model in accordance with the context structure.Wavelet-domain Hidden Markov Tree Model Image segmentation results will be easier toexist the direction of the edge components prone to ambiguity and singularity of diffusion,Contourlet transform can fully capture the higher dimensional and singular image, to obtainthe initial scale the beginning of segmentation by Contourlet domain hidden Markov treemodel, to use the context of the scale adaptive fusion method, to integrate into the smallestscale, that is, pixel-level segmentation from the appropriate coarse scale segmentation results,in order to obtain the final result of image segmentation and the desired results. Lay a solidfoundation for further recognition. The goal of segment the information can also betransimitted rapidly, and the remote real-time monitor can be realized.
Finally, this article proposes reconstruction method, based on Contourlet transform, oforthogonal matching pursuit compressed sensing image. Compressed sensing system uses apriori knowledge by sparse representation of the image, to restructure the original image froma small number of observations, thus to break the limit of sample rate of the Nyquist samplingtheorem. Currently the compressed sensing systems usually use orthogonal wavelet with threedirections to get image, applies the iterative shrinkage method for solving the problem ofcorresponding optimization. The shortage of this method is slow convergence speed, and thereconstruction image has obvious pseudo-Gibbs effect. Coefficients of Contourlet transformis sparser than the wavelet coefficients, and can reconstruct with fewer coefficients of thesame quality images, and through orthogonal matching pursuit force iterative termination inorder to increase the efficiency. It uses complex calculations to make up for short-board ofhardware resources to reduce the requirements of accessing panoramic images of thehardware resource.
引文
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