Mathematical Problems in Engineering
Volume 2010 (2010), Article ID 693532, 14 pages
Research Article

Adaptive Image Enhancement Algorithm Combining Kernel Regression and Local Homogeneity

Faculty of Science and State Key Laboratory for Manufacturing Systems Engineering, Science and Technology Department, Xi'an Jiaotong University, Xi'an 710049, China

Received 21 October 2010; Accepted 15 December 2010

Academic Editor: Paulo Batista Gonçalves

Copyright © 2010 Yu-Qian Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.