关键词: 图像修补, 冗余字典, 稀疏表示

Abstract: Based on the sparse representation theory, this paper proposes an image inpainting framework by using different redundant dictionaries. First of all, it utilizes the Discrete Cosine Transform（DCT） or K-Singular Value Decomposition（K-SVD） to learn three different dictionaries, i.e., the redundant DCT dictionary, K-SVD global（KSVDG） dictionary and K-SVD Adaptive（KSVDA） dictionary. Then, the image to be inpainted is sparsely represented by using these three different redundant dictionaries, respectively. At last, based on the redundant dictionaries and sparse coefficients, the missing part of the image can be well expressed. Experimental results show that the proposed algorithm achieves good visual effects. Further, compared with several existing classical image inpainting methods, it produces superior results in terms of some main image quality evaluation indexes such as peak signal-to-noise ratio and feature similarity.

Key words: image inpainting, redundant dictionary, sparse representation