SAR Despeckling Based on CNN and Bayesian Estimator in Complex Wavelet Domain
Ramin Farhadiani, Saeid Homayouni, Avik Bhattacharya, Masoud Mahdianpari · IEEE Geoscience and Remote Sensing Letters · 2022
We propose a hybrid algorithm for despeckling the Synthetic Aperture Radar (SAR) images using the Convolutional Neural Network (CNN) denoising and complex wavelet shrinkage. In particular, we perform the speckle reduction process in the complex wavelet domain. We first despeckled the approximation complex wavelet coefficients using the MUltichannel LOgarithm with the Gaussian denoising algorithm (MuLoG) based on a pre-trained CNN model named FFDNet. Next, we despeckled the log-transformed details of the complex wavelet coefficients using the averaged version of the Maximum a Posteriori (AMAP) estimator. The experimental results on simulated and real SAR images showed that the proposed method achieved better speckle suppression in the homogeneous areas while preserving edges and point targets than other state-of-the-art methods.