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The total variation-based image denoising model has been generalized andextended in numerous ways, improving its performance in different contexts. Wepropose a new penalty function motivated by the recent progress in thestatistical literature on high-dimensional variable selection. Using aparticular instantiation of the majorization-minimization algorithm, theoptimization problem can be efficiently solved and the computational procedurerealized is similar to the spatially adaptive total variation model. Ourtwo-pixel image model shows theoretically that the new penalty function solvesthe bias problem inherent in the total variation model. The superiorperformance of the new penalty is demonstrated through several experiments. Ourinvestigation is limited to "blocky" images which have small total variation.

Total Variation, Adaptive Total Variation and Nonconvex Smoothly Clipped Absolute Deviation Penalty for Denoising Blocky Images