SAR Image De-noising Using Bivariate Shrinkage Functions and Dual-tree Complex Wavelet
Yu Qing He · Jisuanji fangzhen · 2008
Bivariate shrinkage functions (BSF) statistically denoted as joint probability density functions (PDF) and noise PDF could be united by MAP to de-noise image. The intensity of speckle was hypothesized to be distributed according to Rayleigh distribution. Then SAR image de-noising modal based on BSF and dual-tree complex wavelet transform (DT-CWT) was constructed and reduced. Local variance estimation and wiener filter were used to estimate noise variance and noisy wavelet coefficients variance, which were used to choose an appreciated threshold to de-noise SAR image. Experiment results demonstrate that PSNR and ENL values of de-noised images are extremely larger than other algorithms and edge features have been perfectly preserved.