SAR Imaging and Despeckling Based on Sparse, Low-Rank, and Deep CNN Priors

Synthetic aperture radar (SAR) generally suffers from enormous strains from large quantities of sampling data and serious interferences from the speckle noise. This letter proposes a novel deep network to address these problems. By utilizing the prior knowledge in a more reasonable way, the proposed network could realize SAR imaging and despeckling with down-sampled data simultaneously. Specifically, we decompose the SAR image in the SAR imaging-despeckling observation model into a sparse matrix and a low-rank matrix, and then establish an optimization problem with the corresponding sparse and low-rank priors. Moreover, the deep convolutional neural networks (CNN) denoiser prior is also introduced to further improve the speckle reduction capability. Then, we devise a deep network called SLRCP-Net to solve this problem. Experiments conducted on real Radarsat-1 down-sampled data demonstrate the validity of SLRCP-Net in SAR imaging and speckle suppression.