2-D Kernel Regression Algorithm for Image Denoising
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.
image denoising 2-D kernel regression wavelet-thresholding
Yunfei Yao Yegang Hu Chunsheng Wang Weiwei Sun
School of Mathematics and Computational ScienceFuyang Normal College Fuyang, 236029, China
国际会议
西安
英文
1537-1542
2012-08-24（万方平台首次上网日期，不代表论文的发表时间）