Feature-Supervision Network for Synthetic Aperture Radar Image Despeckling

Speckle noise significantly affects synthetic aperture radar (SAR) imaging systems, causing difficulties in the postprocessing of SAR images. To strike a balance between denoising performance and detail retention, this study proposes an innovative deep learning despeckling network based on feature supervision. The network aims to enhance the noise reduction capability by applying regularized supervision to shallow features within the network. With this enhancement, a robust feature extraction module (RFEM) helps further improve the feature-capture capability by decomposing the image into different frequency components. Subsequently, by integrating complementary prior information captured from different network architectures, we introduced a deep cross-denoising module (DCDM) to enrich the feature details. Furthermore, the incorporation of an attention mechanism enables the network to concentrate more on the information-rich regions of interest, thereby further refining and enhancing the detailed features of the image. The experimental results demonstrate the superiority of the proposed network in effective SAR image despeckling while preserving the image details.