A method for remote sensing image restoration based on the system degradation model

High-definition remote sensing images have been widely used in many fields such as urban planning and resource exploration. Due to the impact of imaging links, the quality of images produced by remote sensing platforms often deteriorates. Therefore, high-performance remote sensing image restoration processing methods are of great significance for improving their application efficiency. However, existing deep learning methods have not been matched and adjusted based on the imaging characteristics and degradation mechanism of remote sensing systems, and lack representation and constraints on various prior information of remote sensing platforms, which leads to false information easily and is not conducive to interpretation application. To overcome these problems, in this work, we conduct research on a multi-stage remote sensing image restoration network. First, we propose a multi-stage network framework of “denoising deblurring detail enhancement”, in which structures at different stages are designed to address the multi-scale characteristics of remote sensing images. Secondly, in order to avoid the degradation of the multi-stage network, we design a differentiated intermediate supervision module and an adaptive structural adjustment module. Finally, based on the degradation characteristics of the remote sensing platform imaging system, we propose a loss function from the prior term of the modulation transfer function. We verify our method by conducting comprehensive comparisons on a new dataset by the full-link imaging simulation. Experiments show that our method is superior to other comparison methods in remote sensing image restoration, and shows competitive performance in terms of restoration performance and speed. Compared with the basic network, the proposed modules improve PSNR and SSIM by 7.18% and 4.74% respectively.