图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。

传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利

用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological

Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解

为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得

到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点-Image inpainting is to fill the missing data in corrupted images and thus to reduce

the information loss of damaged image. Traditional inpainting algorithms are dependent on specific

structure of target images compressive sensing theory makes is possible to realized image

inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD

and MCA algorithm, which first decomposes the image into texture part and structure part, and

then trains the two dictionaries for these two parts with KSVD and reconstructs the original image

with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better

adaptability and performance as compared with traditional algorithms.