空间超分辨率图像重建算法研究
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摘要
图像的空间超分辨率重建是指从一幅或相关的多幅低分辨率图像中重建出高分辨率图像或图像序列的过程，它广泛应用于医学图像分析、视频监控、高清电视转换、遥感测绘、天文观测及星像定位等几乎所有数字图像处理领域，有着广阔的应用前景和巨大的社会经济效益。同时，由于超分辨率图像重建本身是个病态逆问题，而且涉及相关技术的交叉联用，该课题仍然面临诸多挑战，成为图像处理技术研究的热点。
本文探讨了图像配准、图像恢复、图像重建等相关理论和方法，对图像空间超分辨率重建的各个环节进行了系统、深入地研究。针对超分辨率重建流程及实际应用的需求，本论文研究的方向包括：(1)单帧图像的超分辨率重建；(2)图像序列的配准和超分辨率重建；(3)超分辨率重建算法的性能及鲁棒性；(4)超分辨率重建技术与应用的结合。针对上述问题，本文的主要研究内容及贡献归纳如下：
1．针对文字图像纹理主要是纵横和对角线方向的特征，提出用可调模板作为卷积核测定图像平滑度的方案，并以此作为先验信息在Bayes框架下提出了一种基于最大后验估计的单帧超分辨率盲重建算法。实验表明，算法对噪声具有鲁棒性，在车牌识别和文档识别应用中有效提高了文字区域图像的正确识别率，同时也适用于一般普通图像。
2．针对短时间间隔拍摄的适用平移旋转模型的图像序列，提出了一种基于Keren配准的快速超分辨率重建算法。该算法先对图像序列采用Keren算法进行配准，再用变换参数将低分辨率图像序列中的像素点映射到高分辨率网格，最后进行像素值的融合和填充。算法对一定范围内的配准误差具有鲁棒性。实验表明，该算法与传统超分辨率重建算法相比在重建效果和执行效率上都具有明显优势。
3．针对不同焦距下拍摄的多分辨率图像序列和更一般化的投影变换模型，提出了基于SIFT特征和Harris角点的超分辨率重建算法。算法首先提取SIFT特征向量或Harris角点作为图像特征，然后对SIFT特征描述向量采用向量夹角余弦法，对Harris角点采用双向邻域互相关法进行初步特征匹配，再采用RANSAC算法消除误配特征点对，提高配准精度，最后基于像素可信度的定义，提出了模板卷积和像素可信度加权平均的两种空洞像素填充策略，实现序列的超分辨率重建。实验表明，在处理缩放尺度较大的多分辨率图像序列时，本文提出的基于SIFT的超分辨率重建算法性能相对较优，并且克服了其他超分辨率重建算法在低分辨率帧数较少可用信息量不足的情况下性能急剧下降的缺陷。
4．针对普通图像配准和重建方法不能处理的天文观测图像，本文从比较分析高精度恒星定位算法着手，提出了一种结合平场校正、自动搜星定位和基于三角形匹配配准的超分辨率重建方法，并用于星系图像的多峰及双峰结构辨识。模拟图像和实际观测图像序列实验均证实了本文算法的有效性和优越性。
Spatial super-resolution(SR) image reconstruction is a process of producing a highresolution image or image sequence from one or more related low resolution images, whichhas a broad application prospect and significant social and economic benefits in almost alldigital image processing application area, including medical image analysis, videosurveillance, high definition TV conversion, remote sensing survey and mapping, astrometryand star centering, etc. Whereas, because SR image reconstruction itself is an ill-posedinverse problem and involves in combination of several related techniques, this technology isstill faced with lots of challenges and becomes one of research hotspots in image processingarea.
In this thesis, related theories and methods such as image registration, image restoration,image reconstruction are discussed and processes in spatial SR image reconstruction aresystematically and deeply studied. According to SR reconstruction process and requirement ofpractical application, the research issues of this thesis include:(1)single-frame SR imagereconstruction;(2)image registration and SR reconstruction for image sequence;(3)performance and robustness of SR reconstruction algorithms;(4)applications of SRreconstruction technology. Motivated by these issues, main contents and contributions of thisthesis are summarized as follows:
1. Considering texture in character images mainly lies in vertical, horizontal and diagonaldirection, an image smoothness measurement by using a flexible template as convolutionkernel is put forward. Further, applying this measurement as priori information, a Maximum aposteriori(MAP)-based single-frame blind SR reconstruction algorithm is proposed under theBayes framework. Experiments show that the proposed algorithm is robust to noise, whichcan improve correct recognition rate of character area image in license plate recognition anddocument recognition applications and also can be applied to general images.
2. Aimed at image sequence captured in short time intervals which can be described bytranslation and rotation model, a fast SR reconstruction algorithm based on Keren registrationis proposed. This method first registers the image sequence by Keren algorithm, then mapspixels in low-resolution(LR) images onto a high-resolution(HR) grid by transformationparameters, and last conducts image fusion and filling to compute pixel values. It is proved tobe robust to registration errors within a certain range. Experimental results show thatcompared with traditional SR reconstruction algorithms, the proposed method has obvious advantages on reconstruction visual effects and execution efficiency.
3. In order to handle multi-resolution image sequence shot under various focal length andextent SR algorithms to more general projection transformation model, SR reconstructionmethods based on Scale Invariant Feature Transform (SIFT) features and Harris corners areintroduce. First, SIFT description vectors or Harris corners are extracted as image features.Then vector angle cosine method for SIFT descriptors or two-way neighborhood cross-correlation method for Harris corners is used as the initial feature matching. Further, toimprove registration accuracy, Random Sample Consensus (RANSAC) is applied foreliminating mismatch features. Finally, pixel reliability is defined, and based on this definitiontwo “holes filling” strategies, including template convolution algorithm and pixel reliabilityweighted algorithm, is proposed. Thus SR reconstruction for the image sequence isaccomplished. Experimental results show that SIFT-based SR reconstruction algorithmsproposed in this thesis have preferable performance when dealing with multi-resolutionsequence with relatively large zoom scale. In addition, the proposed method overcomes thedeficiency of performance degradation in the case that LR frames is few and availableinformation is insufficient, which many other SR reconstruction methods suffer from.
4. For astronomical image sequences which cannot be directly processed by commonimage registration and reconstruction methods, based on the comparison and analysis of highaccuracy star centering algorithms, a star image SR reconstruction method combined flat fieldcorrection, automatic star search and centering and triangle-match-based registration isproposed and applied to the recognition problem on multi-peak or double-peak structure ofgalaxy and stars. Both simulated and real image sequence experiments verify the effectivenessand superiority of the proposed method.
本文探讨了图像配准、图像恢复、图像重建等相关理论和方法，对图像空间超分辨率重建的各个环节进行了系统、深入地研究。针对超分辨率重建流程及实际应用的需求，本论文研究的方向包括：(1)单帧图像的超分辨率重建；(2)图像序列的配准和超分辨率重建；(3)超分辨率重建算法的性能及鲁棒性；(4)超分辨率重建技术与应用的结合。针对上述问题，本文的主要研究内容及贡献归纳如下：
1．针对文字图像纹理主要是纵横和对角线方向的特征，提出用可调模板作为卷积核测定图像平滑度的方案，并以此作为先验信息在Bayes框架下提出了一种基于最大后验估计的单帧超分辨率盲重建算法。实验表明，算法对噪声具有鲁棒性，在车牌识别和文档识别应用中有效提高了文字区域图像的正确识别率，同时也适用于一般普通图像。
2．针对短时间间隔拍摄的适用平移旋转模型的图像序列，提出了一种基于Keren配准的快速超分辨率重建算法。该算法先对图像序列采用Keren算法进行配准，再用变换参数将低分辨率图像序列中的像素点映射到高分辨率网格，最后进行像素值的融合和填充。算法对一定范围内的配准误差具有鲁棒性。实验表明，该算法与传统超分辨率重建算法相比在重建效果和执行效率上都具有明显优势。
3．针对不同焦距下拍摄的多分辨率图像序列和更一般化的投影变换模型，提出了基于SIFT特征和Harris角点的超分辨率重建算法。算法首先提取SIFT特征向量或Harris角点作为图像特征，然后对SIFT特征描述向量采用向量夹角余弦法，对Harris角点采用双向邻域互相关法进行初步特征匹配，再采用RANSAC算法消除误配特征点对，提高配准精度，最后基于像素可信度的定义，提出了模板卷积和像素可信度加权平均的两种空洞像素填充策略，实现序列的超分辨率重建。实验表明，在处理缩放尺度较大的多分辨率图像序列时，本文提出的基于SIFT的超分辨率重建算法性能相对较优，并且克服了其他超分辨率重建算法在低分辨率帧数较少可用信息量不足的情况下性能急剧下降的缺陷。
4．针对普通图像配准和重建方法不能处理的天文观测图像，本文从比较分析高精度恒星定位算法着手，提出了一种结合平场校正、自动搜星定位和基于三角形匹配配准的超分辨率重建方法，并用于星系图像的多峰及双峰结构辨识。模拟图像和实际观测图像序列实验均证实了本文算法的有效性和优越性。
Spatial super-resolution(SR) image reconstruction is a process of producing a highresolution image or image sequence from one or more related low resolution images, whichhas a broad application prospect and significant social and economic benefits in almost alldigital image processing application area, including medical image analysis, videosurveillance, high definition TV conversion, remote sensing survey and mapping, astrometryand star centering, etc. Whereas, because SR image reconstruction itself is an ill-posedinverse problem and involves in combination of several related techniques, this technology isstill faced with lots of challenges and becomes one of research hotspots in image processingarea.
In this thesis, related theories and methods such as image registration, image restoration,image reconstruction are discussed and processes in spatial SR image reconstruction aresystematically and deeply studied. According to SR reconstruction process and requirement ofpractical application, the research issues of this thesis include:(1)single-frame SR imagereconstruction;(2)image registration and SR reconstruction for image sequence;(3)performance and robustness of SR reconstruction algorithms;(4)applications of SRreconstruction technology. Motivated by these issues, main contents and contributions of thisthesis are summarized as follows:
1. Considering texture in character images mainly lies in vertical, horizontal and diagonaldirection, an image smoothness measurement by using a flexible template as convolutionkernel is put forward. Further, applying this measurement as priori information, a Maximum aposteriori(MAP)-based single-frame blind SR reconstruction algorithm is proposed under theBayes framework. Experiments show that the proposed algorithm is robust to noise, whichcan improve correct recognition rate of character area image in license plate recognition anddocument recognition applications and also can be applied to general images.
2. Aimed at image sequence captured in short time intervals which can be described bytranslation and rotation model, a fast SR reconstruction algorithm based on Keren registrationis proposed. This method first registers the image sequence by Keren algorithm, then mapspixels in low-resolution(LR) images onto a high-resolution(HR) grid by transformationparameters, and last conducts image fusion and filling to compute pixel values. It is proved tobe robust to registration errors within a certain range. Experimental results show thatcompared with traditional SR reconstruction algorithms, the proposed method has obvious advantages on reconstruction visual effects and execution efficiency.
3. In order to handle multi-resolution image sequence shot under various focal length andextent SR algorithms to more general projection transformation model, SR reconstructionmethods based on Scale Invariant Feature Transform (SIFT) features and Harris corners areintroduce. First, SIFT description vectors or Harris corners are extracted as image features.Then vector angle cosine method for SIFT descriptors or two-way neighborhood cross-correlation method for Harris corners is used as the initial feature matching. Further, toimprove registration accuracy, Random Sample Consensus (RANSAC) is applied foreliminating mismatch features. Finally, pixel reliability is defined, and based on this definitiontwo “holes filling” strategies, including template convolution algorithm and pixel reliabilityweighted algorithm, is proposed. Thus SR reconstruction for the image sequence isaccomplished. Experimental results show that SIFT-based SR reconstruction algorithmsproposed in this thesis have preferable performance when dealing with multi-resolutionsequence with relatively large zoom scale. In addition, the proposed method overcomes thedeficiency of performance degradation in the case that LR frames is few and availableinformation is insufficient, which many other SR reconstruction methods suffer from.
4. For astronomical image sequences which cannot be directly processed by commonimage registration and reconstruction methods, based on the comparison and analysis of highaccuracy star centering algorithms, a star image SR reconstruction method combined flat fieldcorrection, automatic star search and centering and triangle-match-based registration isproposed and applied to the recognition problem on multi-peak or double-peak structure ofgalaxy and stars. Both simulated and real image sequence experiments verify the effectivenessand superiority of the proposed method.
引文
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