Adaptive Image Enhancement bining Kernel Regression and Local Homogeneity.pdf.pdf

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2010, Article ID693532,14pagesdoi: ArticleAdaptive Image Enhancement bining Kernel Regression and LocalHomogeneityYu-Qian Yang, Jiang-She Zhang, and Xing-Fang HuangFaculty of Science and State Key Laboratory for Manufacturing Systems Engineering,Science and Technology Department, Xi’an Jiaotong University, Xi’an 710049, ChinaCorrespondence should be addressed to Yu-Qian Yang,******@ 21 October 2010; Accepted 15 December 2010Academic Editor: Paulo Batista Gonc?alvesCopyrightq2010 Yu-Qian Yang et al. This is an open access article distributed under the mons Attribution License, which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly is known that many image enhancement methods have a tradeo?between noise suppressionand edge enhancement. In this paper, we propose a new technique for image enhancement?ltering and explain it in human visual perception theory. bines kernel regression andlocal homogeneity and evaluates the restoration performance of smoothing method. First, imageis ?ltered in kernel regression. Then image local putation is introduced whicho?ers adaptive selection about further smoothing. The overall e?ect of this algorithm is e?ectiveabout noise reduction and edge enhancement. Experiment results show that this algorithm hasbetter performance in image edge enhancement, contrast enhancement, and noise . IntroductionThe presence of noise in image is a major problem that typically negatively a?ects imageanalysis and interpretation process. Therefore, to improve the performance of higher levelprocessing stages, a ?lter method has to be applied in order to reduce noise, enhance edges,and consequently to obtain a better estimate of the ideal image. The purpose of smoothingis of twofold; noise is eliminated to facilitate further processing, and features irrelevant toa