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

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 partic...

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Veröffentlicht in:Pattern recognition Jg. 43; H. 8; S. 2609 - 2619
Hauptverfasser: Chopra, Aditya, Lian, Heng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.08.2010
Elsevier
Schlagworte:
Adaptive algorithms
Algorithms
Applied sciences
Blocking
Detection, estimation, filtering, equalization, prediction
Deviation
Exact sciences and technology
Image processing
Information, signal and communications theory
Miscellaneous
MM algorithm
Optimization
Pattern recognition
Penalty function
SCAD penalty
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Total variation denoising
MM algorithm
Total variation denoising
SCAD penalty
Performance evaluation
Penalty function
Image processing
Noise reduction
Signal processing
Algorithm
Adaptive method
Optimization
Image denoising
ISSN:0031-3203, 1873-5142
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2010.03.022