SAR image Despeckling based on Wavelet Kernel Transform and Gaussian Scale Mizture Model

摘要：

A new method about SAR image despeckling is proposed in this paper,this method is achieved by combining wavelet kernel transform (WKT) and Gaussian Scale Mixture model (GSM).WKT is a multiscale transform which is based on machine learning model.By analysis the distribution of the coefficients after WKT,these coefficients are similar to Gaussian distribution,and these noised coefficients are distributed as Gaussian too,but are independence with non-noised coefficients.In this paper,we construct the neighbor model based on the coefficients after WKT,and use the Bayes least mean square to despeckle the spots in SAR images,and the model describes the edge distribution of coefficients.We use the proposed method to process the SAR images,and the results demonstrate that this method can obtain better denoising images than Lee filter,wavelet transform etc.

关键词： speckle noise wavelet kernel transform Gaussian scale mizture model atrous algorithm

作者: Fan Liu Licheng Jiao Shuyuan Yang

作者单位: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China,Institute of Intelligent Information Processing,Xidian University,Xian 710071,P.R.China

会议类型: 国际会议

会议名称: 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar(第二届亚太合成孔径雷达会议)

会议地点: 西安

会议语种:英文

页码: 1088-1091

在线出版日期: 2009-10-26（万方平台首次上网日期，不代表论文的发表时间）