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. 1. Introduction The presence of noise in image is a major problem that typically negatively affects image analysis and interpretation process. Therefore, to improve the performance of higher level processing stages, a filter 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 smoothing is of twofold; noise is eliminated to facilitate further processing, and features irrelevant to a given problem are ruled out to reduce the complexity for the subsequent processing. The designs for filters have been conducted for a long time. Many of them may enable to perform some filtering functions with loss of useful information at the same time. In other words, these filters are appropriate when both smoothing and discontinuity preservation objectives are desired. The literature on signal and image filter is vast, and comprehensive review is beyond the scope of this paper. We only introduce several important nonlinear image filter methods which are relevant to our method. PDE-based image processing methods became widespread after the work of Perona and Malik [1], where they proposed a modified version of the heat equation called anisotropic diffusion that adapted the diffusivity to image features. The anisotropic diffusion equation is also the first variation of an image energy that seeks piecewise constant solutions. A multitude of nonlinear PDE models have been developed for a wide variety of images and applications [2–5]. The nonlinear PDE models have proven to be effective, but only for particular applications where the input data is well suited to the model's underlying geometric assumptions. The parameter tuning is a challenge because it entails fuzzy thresholds that determine which image features are enhanced and which are smoothed away.
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