2-D Kernel Regression Algorithm for Image Denoising

Abstract

Removing noise from the original image plays an important role in many important applications involving image-based medical diagnosis and visual material examination for public security, and so on. Among them, there have been several published methods to solve the related problem, however, each approach has its advantages, and limitations. This paper examines a new measure of denosing in space domain based on 2-D kernel regression which overcomes the difficulties found in other measures. The idea of this method mainly let the values of a row or a column from an image are taken as the measured results of a fitting function. The following step is to estimate the weight coefficients using least square method. Finally, we obtain an denoised image by resampling the estimated function, and the variable x denotes the coordinate of an image. Results of an experimental applications of this method analysis procedure are given to illustrate the proposed technique, and compared with the basic wavelet-thresholding algorithm for image denoising.