Adaptive Image Enhancement Algorithm Combining Kernel Regression and Local Homogeneity

It is known that many image enhancement methods have a tradeoff between noise suppression and edge enhancement. In this paper, we propose a new technique for image enhancement filtering and explain it in human visual perception theory. It combines kernel regression and local homogeneity and evaluate...

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Bibliographic Details
Published inMathematical Problems in Engineering Vol. 2010; pp. 1536 - 1549-188
Main Authors Yang, Yu-Qian, Zhang, Jiang-She, Huang, Xing-Fang
Format Journal Article
LanguageEnglish
Published New York Hindawi Limiteds 01.01.2010
Hindawi Publishing Corporation
Hindawi Limited
Subjects
Adaptive algorithms
Engineering
Homogeneity
Human performance
Image contrast
Image enhancement
Image filters
Methods
Noise
Noise reduction
Performance evaluation
Regression
Smoothing
Studies
Visual perception
Visual perception driven algorithms
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Summary:It is known that many image enhancement methods have a tradeoff between noise suppression and edge enhancement. In this paper, we propose a new technique for image enhancement filtering and explain it in human visual perception theory. It combines kernel regression and local homogeneity and evaluates the restoration performance of smoothing method. First, image is filtered in kernel regression. Then image local homogeneity computation is introduced which offers adaptive selection about further smoothing. The overall effect of this algorithm is effective about noise reduction and edge enhancement. Experiment results show that this algorithm has better performance in image edge enhancement, contrast enhancement, and noise suppression.
ISSN:1024-123X
1563-5147
DOI:10.1155/2010/693532