Abstract
Natural image statistics indicate that we should use non-convex norms for most regularization tasks in image processing. Therefore, in order to restore the damaged image better, this paper proposes a new non-convex high variational image inpainting model. An iteratively reweighed minimization method is proposed to tackle this non-convex problem. Experimental results show that the proposed method yields quantitative and qualitative improvements compared to the current Total Variation (TV) method and convex second Total Generalized Variation (TGV) inpainting method.
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