Abstract: This paper proposed an image inpainting algorithm based on self-adaptive group sparse representation to solve the problem that the texture and structure clarity are poor in the repair results.Due to the difference of texture and structure information in the natural images,in order to distinguish the group structure with thefixed image block size in original algorithm,firstly,a method is proposed to adaptively select the size of sample image patch to construct an adaptive group structure.Secondly,singular value decomposition is conducted in groups to obtain an adaptive learning dictionary of the image patch group,and the Split Bregman Iteration algorithm is used to solve the objective cost function.Finally,the adaptive dictionary and the sparse coding coefficient of each group are updated by adjusting the number of image patches and iterations in the group to get a better restoration effect.The experimental results show that this method not only improves the peak signal to noise ratio and feature similarity index of image,but also improves the repair efficiency.

Key words: Image inpainting, Self-adaptive group, Self-adaptive learning dictionary, Sparse representation