Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

32 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2609-2619
Journal / PublicationPattern Recognition
Volume43
Issue number8
Publication statusPublished - Aug 2010
Externally publishedYes

Abstract

The total variation-based image denoising model has been generalized and extended in numerous ways, improving its performance in different contexts. We propose a new penalty function motivated by the recent progress in the statistical literature on high-dimensional variable selection. Using a particular instantiation of the majorization-minimization algorithm, the optimization problem can be efficiently solved and the computational procedure realized is similar to the spatially adaptive total variation model. Our two-pixel image model shows theoretically that the new penalty function solves the bias problem inherent in the total variation model. The superior performance of the new penalty function is demonstrated through several experiments. Our investigation is limited to "blocky" images which have small total variation. © 2010 Elsevier Ltd. All rights reserved.

Research Area(s)

  • MM algorithm, SCAD penalty, Total variation denoising