OGSRN: Optical-guided super-resolution network for SAR image

Abstract

Although Convolutional Neural Networks (CNNs) have significantly improved the development of image Super-Resolution (SR) technology in recent years, the existing SR methods for SAR image with large scale factors have rarely been studied due to technical difficulty. A more efficient method is to obtain comprehensive information to guide the SAR image reconstruction. Indeed, the co-registered High-Resolution (HR) optical image has been successfully applied to enhance the quality of SAR image due to its discriminative characteristics. Inspired by this, we propose a novel Optical-Guided Super-Resolution Network (OGSRN) for SAR image with large scale factors. Specifically, our proposed OGSRN consists of two sub-nets: a SAR image Super-Resolution U-Net (SRUN) and a SAR-to-Optical Residual Translation Network (SORTN). The whole process during training includes two stages. In stage-1, the SR SAR images are reconstructed by the SRUN. And an Enhanced Residual Attention Module (ERAM), which is comprised of the Channel Attention (CA) and Spatial Attention (SA) mechanisms, is constructed to boost the representation ability of the network. In stage-2, the output of the stage-1 and its corresponding HR SAR images are translated to optical images by the SORTN, respectively. And then the differences between SR images and HR images are computed in the optical space to obtain feedback information that can reduce the space of possible SR solution. After that, we can use the optimized SRUN to directly produce HR SAR image from Low-Resolution (LR) SAR image in the testing phase. The experimental results show that under the guidance of optical image, our OGSRN can achieve excellent performance in both quantitative assessment metrics and visual quality.