SAR Image Despeckling Based on Local Translation-Rayleigh Distribution Model
Feng Hong-xiao; Jiao Li-cheng; Hou Biao
Institute of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an 710071, China
Abstract：Based on the statistical model in stationary wavelet domain, an algorithm of SAR image despeckling is developed. Firstly, nonlogarithmic additive model is applied to SAR image, and then a statistical distribution—Local Translation-Rayleigh Distribution Model (LTRDM) is proposed for the noise within nonlogarithmic additive model in the image domain. Finally, based on this model and in the stationary wavelet domain, the solution of real signal coefficients are given by using Maximum A Posteriori(MAP). Experiments show that local translation-Rayleigh distribution model is effective, and also indicate that a despeckling algorithm based on LTRDM proposed in this paper is robust, and possess high performance over many traditional algorithms.
2010, Vol. 32 
