【作者】 邢海霞；

【导师】 祝轩；

【作者基本信息】 西北大学 ， 电子与通信工程， 2011， 硕士

【摘要】 图像修补技术是图像处理的关键技术之一,被广泛地应用于生物视觉系统研究、计算机模式识别和医学等多个领域。目前,基于稀疏表示的图像修补研究是该领域近几年的一个新的研究分支,同时,稀疏表示的图像修补研究具有很重要的现实意义和广阔的研究前景,基于此,本文在以下几方面进行了研究,并有所收获：1、系统地研究了MCA稀疏模型,实现了基于MCA模型的图像稀疏分解,并数值实现MCA和TV模型相结合的污损图像修补。更进一步指出MCA模型可以和CDD模型相结合修补污损图像,并算法实现。实验证明,MCA&CDD修补算法在一定程度上克服了MCA&TV的弊病,能够较好修补污损图像。2、研究VO模型与Bregman迭代理论,利用Bregman迭代求解VO模型,实现基于VO模型的图像分解,并将VO模型、曲波变换、局域DCT变换与CDD模型相结合实现基于VO&CDD的稀疏图像修补,实验证明,VO&CDD可以较好地实现稀疏图像修补。3、引入稀疏表示与全变分相结合的图像分解模型(SAT模型),并利用Bregman迭代方法求解该模型,将其应用于稀疏分解与稀疏图像修补。实验证明,SAT模型能够获得好的重构图像,并具有好的图像稀疏修补能力。总之,本文重点研究了稀疏分解模型以及基于稀疏表示的图像修补方法,并算法实现了MCA&TV、MCA&CDD和VO&CDD修补算法,更进一步采用SAT模型实现图像稀疏分解与修补,分别获得好的修补结果。更多还原

【Abstract】 As a key technology of image processing, image inpainting has been widely used in the research of biological vision systems, computer pattern recognition, medicine and other fields. At present, the research of image inpainting based on sparse representation is a new branch of this field in recent years. The image inpainting of sparse representation has very crucial practical significance and broad researching prospects. According to this, this paper mainly tries to do some research from the following aspects and concludes.1. The paper systematically studies the Morphological Component Analysis (MCA) sparse model, achieves the sparse decomposition of image based on the model of MCA and deals with the dirty image inpainting by combining the model of MCA &TV. Furthermore, the paper points out that the model of MCA could be combined with CDD to achieve the same result. The experiment turned out that the combination of MCA&CDD overcomes the shortcomings of MCA&TV in some extent and has a better performance in scratched image inpainting.2. The paper studies the VO model and the theory of Bregman iteration. Through bregman iterative, it solves VO model, thereby, achieve image sparse decomposition based on VO model. Combining the VO model, curvelet transform, local DCT transform with CDD model to achieve sparse image inpainting based on VO&CDD algorithm. Finally, the experiment shows that VO&CDD can achieve better sparse image inpainting.3. The paper introduces a new image decomposition model, named SAT, which combines the sparse representation with total variation. Using bregman iteration method solves this model and then applies to sparse decompose image and inpainting. The test shows that the SAT model can obtain almost perfect reconstructed image and has a better ability to inpainting the sparse image.In a word, this paper mainly studies the sparse decomposition model and the image inpainting based on sparse representation methods, and achieves the algorithms for the MCA&TV, MCA&CDD and VO&CDD inpainting and furthermore, achieves the image decomposition and sparse inpainting through the SAT model. Each of them carries out good performance.更多还原