Self-adaptive group based sparse representation for image inpainting
LIN Jinyong1, DENG Dexiang1, YAN Jia1, LIN Xiaoying2
1. School of Electronic Information, Wuhan University, Wuhan Hubei 430072, China; 2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Abstract：Focusing on the problem of object structure discontinuity and poor texture detail occurred in image inpainting, an inpainting algorithm based on self-adaptive group was proposed. Different from the traditional method which uses a single image block or a fixed number of image blocks as the repair unit, the proposed algorithm adaptively selects different number of similar image blocks according to the different characteristics of the texture area to construct self-adaptive group. A self-adaptive dictionary as well as a sparse representation model was established in the domain of self-adaptive group. Finally, the target cost function was solved by Split Bregman Iteration. The experimental results show that compared with the patch-based inpainting algorithm and Group-based Sparse Representation (GSR) algorithm, the Peak Signal-to-Noise Ratio (PSNR) and the Structural SIMilarity (SSIM) index are improved by 0. 94-4.34 dB and 0. 0069-0.0345 respectively; meanwhile, the proposed approach can obtain image inpainting speed-up of 2.51 and 3.32 respectively.
林金勇, 邓德祥, 颜佳, 林晓英. 基于自适应相似组稀疏表示的图像修复算法[J]. 计算机应用, 2017, 37(4): 1169-1173.
LIN Jinyong, DENG Dexiang, YAN Jia, LIN Xiaoying. Self-adaptive group based sparse representation for image inpainting. Journal of Computer Applications, 2017, 37(4): 1169-1173.
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2017, Vol. 37 
