期刊
PATTERN RECOGNITION
卷 43, 期 8, 页码 2609-2619出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2010.03.022
关键词
MM algorithm; SCAD penalty; Total variation denoising
资金
- Singapore MOE
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. (C) 2010 Elsevier Ltd. All rights reserved.
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