The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.
deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling