WEIGHTED SCHATTEN P-NORM MINIMIZATION WITH LOCAL AND NONLOCAL CONSTRAINTS FOR NOISY IMAGE COMPLETION

Weighted Schatten p-norm minimization (WSNM) has been used successfully for noisy-free image completion. However, WSNM can introduce extra artifacts if the observed entries of image contain noise. In this paper, we present a novel WSNM-based method for noisy image completion, which incorporates both local smoothness and nonlocal self-similarity in a unified framework. More concretely, the analysis operator is utilized to ensure local smoothness and the nonlocal statistical modeling (NLSM) is adopted to constrain nonlocal self-similarity while WSNM is effective for completing the missing entries. To make the proposed method tractable and robust, the alternating direction method of multipliers (ADMM) is employed to solve the above inverse problem. Experimental results show the effectiveness of the proposed method for noisy image completion.