LAUSR

Synthetic aperture radar (SAR) images are mainly corrupted by speckle noise, which needs to be removed for further processing. In this letter, we propose an attention and gradient-based SAR denoising network (AGSDNet) to remove speckle noise from SAR images while preserving finer details. In the proposed network, gradient information of the noisy image is first concatenated with its features in order to increase the feature information content (map). An intermediate feature denoising block (FDB) is then employed to reduce noise from this feature map. Finally, two attention blocks, designed and deployed, in the network focus on preserving the more informative features in the image thereby generating a feature preserved denoised image. The proposed network is compared with several classical and deep learning-based SAR denoising methods to demonstrate its superiority in terms of qualitative as well as quantitative measures. We have provided the training and testing code of AGSDNet at https://github.com/RTSIR/AGSDNet.