A new homomorphic Bayesian wavelet-based MMAE filter for despeckling SAR images

In this paper, we introduce a new homomorphic Bayesian wavelet-based minimum mean absolute error filter for despeckling synthetic aperture radar (SAR) images. The wavelet coefficients of the logarithmically transformed reflectance image and the speckle noise image are modeled using a Cauchy prior and an additive white Gaussian noise distribution, respectively. These models are then exploited to develop a Bayesian minimum mean absolute error estimator. A method is presented for estimating the dispersion parameter of the assumed Cauchy prior. Experiments are carried out on a typical noise-free image corrupted with synthetic speckle noise and a real SAR image, and the results show that the proposed method has a performance that is superior to that of the other existing methods in terms of the peak signal-to-noise ratio, ability to suppress the speckle in the homogeneous regions and the visual quality.