  Synthetic Aperture Radar (SAR) despeckling is an important problem in remote
sensing as speckle degrades SAR images, affecting downstream tasks like
detection and segmentation. Recent studies show that convolutional neural
networks(CNNs) outperform classical despeckling methods. Traditional CNNs try
to increase the receptive field size as the network goes deeper, thus
extracting global features. However,speckle is relatively small, and increasing
receptive field does not help in extracting speckle features. This study
employs an overcomplete CNN architecture to focus on learning low-level
features by restricting the receptive field. The proposed network consists of
an overcomplete branch to focus on the local structures and an undercomplete
branch that focuses on the global structures. We show that the proposed network
improves despeckling performance compared to recent despeckling methods on
synthetic and real SAR images.
