序列图像超分辨率重建技术研究
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摘要
近年来，随着科学研究和实用技术的不断深入发展，人们对高分辨率视频和图像序列的需求日益增长。但是，在实际的获取过程中，这些图像和视频通常会受到多种降质因素的影响，难以满足实际应用的需要，超分辨率重建技术(Super-resolution，SR)应运而生。超分辨率重建技术可以在现有硬件水平下对已获得的图像和视频进行处理以提高它们的分辨率。这类方法不需要耗费高昂的成本，又可以取得较好的处理效果，具有很高的科研价值和商业利益，因此获得了广泛的关注。
本论文首先介绍超分辨率重建技术的研究背景和发展动态；其次系统分析了序列图像超分辨率重建的理论基础、系统架构和方法类别；随后以基于空域的序列图像超分辨率重建方法研究为主线，重点研究运动估计、局部几何边缘结构特性保持、参数自适应设置以及彩色图像重建等问题。在此基础上，本论文提出多个算法，并取得一定的研究成果和理论创新。论文取得的研究成果和创新点主要包括：
1.提出一种序列图像配准算法。该算法首先基于小波多分辨的思想，生成图像金字塔，相对于图像中逐点搜索的配准方法，减小配准过程中的搜索空间，有效提高配准的速度。然后通过由局部灰度熵准则、最大相关系数度量准则和简化欧式距离比例不变准则构成的改进多约束准则鉴别项提取同名特征点，剔除虚假匹配特征点对的影响，并依据金字塔结构逐步精确化配准结果，同时通过最小二乘法计算配准参数。实验证明，本算法配准误差较小，精度较高，速度较快，同时具有较高的可靠性。
2.提出一种基于全变分正则化模型的序列图像超分辨率重建算法。该算法针对数字全变分方法在图像重建中存在的不足，设计基于图像局部结构信息的局部自适应度量函数作为各向异性耦合系数，对低阶全变分模型和超数字化全变分模型进行耦合，构建基于自适应耦合局部数字全变分的正则化模型并用于超分辨率重建。该方法在重建过程中不仅能够抑制噪声，保持图像边缘等重要几何结构，而且能够抑制阶梯效应，并能提高图像的整体清晰度和保真度。
3.提出一种基于学习和稀疏表示的高分辨率迭代初始图像生成算法。该算法首先通过非均匀插值方法融合低分辨率图像序列各帧信息生成高分辨率初始插值图像，然后利用基于学习方法构建的过完备稀疏表示字典估计该插值图像的高分辨率先验图像，再将二者叠加作为用于迭代重建算法的高分辨率初始图像。该算法充分利用低分辨率图像序列的信息和基于学习方法提供的高频信息，有效提高高分辨率重建初始图像的质量，对增强重建图像效果有一定的帮助。
4.提出一种基于MAP的自适应正则化超分辨率重建算法。该算法首先利用基于学习的方法生成高分辨率迭代初始图像，其次利用低分辨率图像序列信息自动获取正则化重建方法的模型参数，增强算法的自适应性并降低正则参数调整的复杂度，然后利用对陡坡边缘保持特性较好的三边滤波模型作为正则项进行超分辨率重建。此外，为消除可能的配准误差对重建图像的影响，采用循环迭代的方式，同时估计配准参数和重建图像。通过对合成图像序列和真实图像序列进行的实验分析，该算法能更好地保持图像的边缘结构信息，并使重建图像的纹理更清晰。
5.提出一种彩色序列图像通道信息联合超分辨率重建算法。该算法利用基于学习的方法提高重建初始图像的质量，同时综合运用图像在RGB色彩空间和YCbCr空间携带的信息，利用RGB信息构造数据保真项，基于YCbCr信息构造亮度正则项和色度正则项。该方法兼顾通道信息间的相关性，从而有效提高彩色图像序列的超分辨率重建效果，对多通道图像的超分辨率重建处理具有一定的参考价值。通过对重建图像峰值信噪比的测试和主观视觉效果的度量验证了该算法的有效性。
With the rapid developments of scientific research and applications, the demandof high-resolution video and image sequences grows fast in recent years. However,images are always affected by various degraded factors in actual acquisition process,and it is difficult to meet the need for practical applications. Super-resolutionreconstruction technology has been proved to be one of the efficient techniques tosolve the above problem. Super-resolution reconstruction fuses a sequence oflow-resolution frames to produce one or a set of high-resolution images at a relativelylow cost. So, it has become one of the hot topics of digital image processing.
Firstly, this dissertation introduced the research background and developmenttrends of super-resolution reconstruction. Secondly, the systematic analysis waspresented including the theoretical basis, system architecture and the main types ofsuper-resolution method. Subsequently, the dissertation focused on several key issuesof image sequence super-resolution reconstruction, such as motion estimation,keeping locally structural characteristics, locally adaptive regularization, adaptiveparameter setting, and color image reconstruction. On this basis, this dissertationproposed a number of algorithms, and achieved certain research results. The maincontributions and innovation points of the dissertation are as follows:
1. To improve the accuracy and speed of image registration, a novel method wasproposed to register consecutive frames based on wavelet transformation andimproved multi-restriction criterion. At first, wavelet image pyramids of the referenceframe and the sensed frame were generated to narrow the search space. The featurepoints were found using Harris detector. Followed by the use of improvedmulti-restriction criterion, matching feature points were extracted from the highestlevel of image pyramids. Least squares technique was employed to calculate theregistered parameters. Then the coarse-to-fine hierarchical strategy was applied. Theestimates of the mapping function parameters were gradually improve by thefollowing levels of the pyramids. Finally, artificial images and actual images wereused to test. Experimental results demonstrated the presented method can quicklyobtain the registration parameters with high accuracy.
2. Based on the total-variation regularization model, a novel super-resolution reconstruction method was proposed. The method combined the ideas of low-ordertotal-variation model and beyond digital total-variation model. A new regularizationnorm was presented, termed as locally adaptive digital total-variation, to keep edgesand more details. The experimental results were introduced to illustrate theeffectiveness of the proposed algorithm. Performance analysis shows that our methodis superior to similar existing methods.
3. Based on learning and sparse representation, a high-resolution iterative initialimage generation method was proposed. Firstly, non-uniform interpolation was usedto fuse the information of low-resolution images to generate a high-resolution initialinterpolated image. Secondly, a high resolution priori image was calculated by aover-complete sparse dictionary. Then, two images were fused as an high-resolutioninitial image which could be used to reconstruct. The method was made full use of thelow-resolution image-sequence information and the high-frequency priori informationto improve the quality of a high-resolution initial reconstruction image. It shouldcertainly help to enhance the reconstructed image. Simulation results confirmed theeffectiveness of this method.
4. An adaptive super-resolution reconstruction method based on trilateralregularization was proposed. To reduce the complexity of regularization parametersadjustment, the parameters were computed through low-resolution image sequencesautomatically. And then, the trilateral regularization function was adopted to keepslope and roof edges. At the same time, the learning-based approach was utilized togenerate a high-resolution iterative initial image. In addition, in order to eliminate theimpact of possible registration errors, the iterative algorithm was used tosimultaneously estimate registration parameters and reconstructed image. Syntheticimage sequences and real image sequences of experiments showed that the methodhas better performance.
5. A method of color super-resolution based on a MAP estimation technique byminimizing a multi-term cost function was proposed. The method integrated theimage information of RGB and YCbCr. The RGB information was used to define thedata fidelity penalty term. The components of YCbCr were employed to generateluminance regularization and chrominance regularization items. Then, thelearning-based method was used to improve the quality of the initial reconstructionimage. A series of numerical experiments were performed to show the effectiveness ofthe proposed approach, both in the visual effect and PSNR.
本论文首先介绍超分辨率重建技术的研究背景和发展动态；其次系统分析了序列图像超分辨率重建的理论基础、系统架构和方法类别；随后以基于空域的序列图像超分辨率重建方法研究为主线，重点研究运动估计、局部几何边缘结构特性保持、参数自适应设置以及彩色图像重建等问题。在此基础上，本论文提出多个算法，并取得一定的研究成果和理论创新。论文取得的研究成果和创新点主要包括：
1.提出一种序列图像配准算法。该算法首先基于小波多分辨的思想，生成图像金字塔，相对于图像中逐点搜索的配准方法，减小配准过程中的搜索空间，有效提高配准的速度。然后通过由局部灰度熵准则、最大相关系数度量准则和简化欧式距离比例不变准则构成的改进多约束准则鉴别项提取同名特征点，剔除虚假匹配特征点对的影响，并依据金字塔结构逐步精确化配准结果，同时通过最小二乘法计算配准参数。实验证明，本算法配准误差较小，精度较高，速度较快，同时具有较高的可靠性。
2.提出一种基于全变分正则化模型的序列图像超分辨率重建算法。该算法针对数字全变分方法在图像重建中存在的不足，设计基于图像局部结构信息的局部自适应度量函数作为各向异性耦合系数，对低阶全变分模型和超数字化全变分模型进行耦合，构建基于自适应耦合局部数字全变分的正则化模型并用于超分辨率重建。该方法在重建过程中不仅能够抑制噪声，保持图像边缘等重要几何结构，而且能够抑制阶梯效应，并能提高图像的整体清晰度和保真度。
3.提出一种基于学习和稀疏表示的高分辨率迭代初始图像生成算法。该算法首先通过非均匀插值方法融合低分辨率图像序列各帧信息生成高分辨率初始插值图像，然后利用基于学习方法构建的过完备稀疏表示字典估计该插值图像的高分辨率先验图像，再将二者叠加作为用于迭代重建算法的高分辨率初始图像。该算法充分利用低分辨率图像序列的信息和基于学习方法提供的高频信息，有效提高高分辨率重建初始图像的质量，对增强重建图像效果有一定的帮助。
4.提出一种基于MAP的自适应正则化超分辨率重建算法。该算法首先利用基于学习的方法生成高分辨率迭代初始图像，其次利用低分辨率图像序列信息自动获取正则化重建方法的模型参数，增强算法的自适应性并降低正则参数调整的复杂度，然后利用对陡坡边缘保持特性较好的三边滤波模型作为正则项进行超分辨率重建。此外，为消除可能的配准误差对重建图像的影响，采用循环迭代的方式，同时估计配准参数和重建图像。通过对合成图像序列和真实图像序列进行的实验分析，该算法能更好地保持图像的边缘结构信息，并使重建图像的纹理更清晰。
5.提出一种彩色序列图像通道信息联合超分辨率重建算法。该算法利用基于学习的方法提高重建初始图像的质量，同时综合运用图像在RGB色彩空间和YCbCr空间携带的信息，利用RGB信息构造数据保真项，基于YCbCr信息构造亮度正则项和色度正则项。该方法兼顾通道信息间的相关性，从而有效提高彩色图像序列的超分辨率重建效果，对多通道图像的超分辨率重建处理具有一定的参考价值。通过对重建图像峰值信噪比的测试和主观视觉效果的度量验证了该算法的有效性。
With the rapid developments of scientific research and applications, the demandof high-resolution video and image sequences grows fast in recent years. However,images are always affected by various degraded factors in actual acquisition process,and it is difficult to meet the need for practical applications. Super-resolutionreconstruction technology has been proved to be one of the efficient techniques tosolve the above problem. Super-resolution reconstruction fuses a sequence oflow-resolution frames to produce one or a set of high-resolution images at a relativelylow cost. So, it has become one of the hot topics of digital image processing.
Firstly, this dissertation introduced the research background and developmenttrends of super-resolution reconstruction. Secondly, the systematic analysis waspresented including the theoretical basis, system architecture and the main types ofsuper-resolution method. Subsequently, the dissertation focused on several key issuesof image sequence super-resolution reconstruction, such as motion estimation,keeping locally structural characteristics, locally adaptive regularization, adaptiveparameter setting, and color image reconstruction. On this basis, this dissertationproposed a number of algorithms, and achieved certain research results. The maincontributions and innovation points of the dissertation are as follows:
1. To improve the accuracy and speed of image registration, a novel method wasproposed to register consecutive frames based on wavelet transformation andimproved multi-restriction criterion. At first, wavelet image pyramids of the referenceframe and the sensed frame were generated to narrow the search space. The featurepoints were found using Harris detector. Followed by the use of improvedmulti-restriction criterion, matching feature points were extracted from the highestlevel of image pyramids. Least squares technique was employed to calculate theregistered parameters. Then the coarse-to-fine hierarchical strategy was applied. Theestimates of the mapping function parameters were gradually improve by thefollowing levels of the pyramids. Finally, artificial images and actual images wereused to test. Experimental results demonstrated the presented method can quicklyobtain the registration parameters with high accuracy.
2. Based on the total-variation regularization model, a novel super-resolution reconstruction method was proposed. The method combined the ideas of low-ordertotal-variation model and beyond digital total-variation model. A new regularizationnorm was presented, termed as locally adaptive digital total-variation, to keep edgesand more details. The experimental results were introduced to illustrate theeffectiveness of the proposed algorithm. Performance analysis shows that our methodis superior to similar existing methods.
3. Based on learning and sparse representation, a high-resolution iterative initialimage generation method was proposed. Firstly, non-uniform interpolation was usedto fuse the information of low-resolution images to generate a high-resolution initialinterpolated image. Secondly, a high resolution priori image was calculated by aover-complete sparse dictionary. Then, two images were fused as an high-resolutioninitial image which could be used to reconstruct. The method was made full use of thelow-resolution image-sequence information and the high-frequency priori informationto improve the quality of a high-resolution initial reconstruction image. It shouldcertainly help to enhance the reconstructed image. Simulation results confirmed theeffectiveness of this method.
4. An adaptive super-resolution reconstruction method based on trilateralregularization was proposed. To reduce the complexity of regularization parametersadjustment, the parameters were computed through low-resolution image sequencesautomatically. And then, the trilateral regularization function was adopted to keepslope and roof edges. At the same time, the learning-based approach was utilized togenerate a high-resolution iterative initial image. In addition, in order to eliminate theimpact of possible registration errors, the iterative algorithm was used tosimultaneously estimate registration parameters and reconstructed image. Syntheticimage sequences and real image sequences of experiments showed that the methodhas better performance.
5. A method of color super-resolution based on a MAP estimation technique byminimizing a multi-term cost function was proposed. The method integrated theimage information of RGB and YCbCr. The RGB information was used to define thedata fidelity penalty term. The components of YCbCr were employed to generateluminance regularization and chrominance regularization items. Then, thelearning-based method was used to improve the quality of the initial reconstructionimage. A series of numerical experiments were performed to show the effectiveness ofthe proposed approach, both in the visual effect and PSNR.
引文
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