Image Inpainting by Characteristic Classification Learning and Patch Sparsity Propagation

Sample image dictionary has poor adaptability and simplex valid information, which results in bad image sparse representation. Because of the shortage, this paper discusses a new image inpainting method by characteristics classification learning and patch sparsity propagation. The proposed method classified the image patches by their different characteristics firstly, then got the corresponding over-complete dictionary by training the image blocks of different characteristics and extracted different valid information from these blocks for sparse coding, which makes the sparse representation to have stronger adaptive capacity. Finally, the propagation mechanisms can be improved by modifying the patch sparsity propagation model. Experiment results show that the proposed method can work on the edge, irregular textures and smooth portion effectively and make the image quality higher.