Speckle Denoising Based on Bivariate Shrinkage Functions and Dual-tree Complex Wavelet Transform

Bivariate shrinkage functions (bsf) statistically denoted as joint probability density functions (pdf) and noise pdf, can be united by MAP to denoise image. Because the intensity of speckle in synthetic aperture radar (SAR) image is hypothesized to be distributed according to Rayleigh distribution, SAR image denoising modal based on bsf and dual-tree complex wavelet transform (DT-CWT) is constructed and reduced. Local variance estimation and wiener filter are used to estimate noise variance and noisy wavelet coefficients variance respectively, and they are used to choose an appreciated threshold to denoise SAR image. Experiment results demonstrate that PSNR and ENL values of denoised images are extremely larger than the speckle denoising algorithms based on discrete wavelet transform (DWT) and edge features have been perfectly preserved.