λ-WMAP: a statistical speckle filter operating in the wavelet domain

In this paper we introduce the Γ-WMAP filter, a wavelet based equivalent to the classical Γ-MAP filter. We model speckle as additive signal-dependent noise, and propose to use the normal inverse Gaussian (NIG) distribution as a statistical model for the wavelet coefficients of both the reflectance image and the noise image. A method for estimating the parameters of the proposed statistical models is presented, and we show that the NIG distribution makes excellent fits to the distributions of the wavelet coefficients of single-look synthetic aperture radar (SAR) images. The performance of the Γ-WMAP filter is tested on three single-look SAR images. We find that when the filter is used in a global mode it may severely blur the image. However, when applied in a local, adaptive mode the new algorithm has excellent de-speckling performance. Visual comparisons with the Γ-MAP filter show that Γ-WMAP tends to give better de-speckling. Quantitative comparisons in homogeneous regions using both the equivalent number of looks and the log standard deviation as measures definitely show that the Γ-WMAP gives better speckle filtering.