SAR-DRDNet: A SAR image despeckling network with detail recovery

Synthetic aperture radar (SAR) image is affected by inherent speckle, greatly hindering its interpretation and subsequent applications. In SAR images despeckling tasks, it is still challenging to remove speckle while preserving spatial details. Traditional methods generally require a complicated design of relation-ships between the speckled and noise-free SAR images. The growing popularity of deep learning algo-rithms exhibits great potential in resolving this limitation. However, the existing deep learning-based despeckling methods need further improvement in speckle suppression and details preservation. Therefore, we propose an end-to-end SAR images despeckling residual network with detail recovery, named SAR-DRDNet, which is mainly composed of non-local blocks (NLBs) and detail recovery blocks (DRBs). NLB takes full advantage of the global information of the SAR image to suppress speckle. DRB is further employed to recover lost details in the process of NLBs, taking into account the multi-scale con-textual information of pixels and has a larger receptive field without reducing the resolution of feature maps. We validate the proposed SAR-DRDNet on the simulated and real SAR data. Both quantitative and qualitative comparisons demonstrate the superiority of the proposed SAR-DRDNet over the many mainstream methods, evidenced by its capability to achieve a great balance between speckle suppression and textural details preservation.(c) 2022 Elsevier B.V. All rights reserved.