Recent results in compressive sensing based image inpainiting algorithms and open problems

Many image inpainiting (IMIN) algorithms have been developed to restore corrupted images in last decades. However, traditional IMIN algorithms do not learn the sparse structure of the corrupted images. Hence, it is very hard to renovate the images accurately. In contrast to the conventional algorithms, compressive sensing based IMIN algorithms can remove strong noise as well as can restore images by virtual of learning the inherent sparse structure in images. This paper introduces recent results in compressive sensing based IMIN algorithms and presents corresponding simulation examples to validate the proposed algorithms. In addition, we also summarize some open problems and point out some potential approaches to solve these problems.