Optical Imaging Degradation Simulation and Transformer-Based Image Restoration for Remote Sensing

Due to atmospheric turbulence, optical system limitations, satellite platform jitter, and other reasons, remote-sensing images inevitably undergo different degrees of degradation. Employing the deep-learning method to improve the on-orbit image quality faces many challenges such as lack of data, limited computing resources, network architecture design, and so on. Among these factors, establishing a physics-guided dataset during the image restoration stage and avoiding unforeseen effects such as ringing pose a significant challenge for remote-sensing image restoration. This letter proposes an optical imaging degradation simulation model and transformer-based algorithm to improve remote-sensing image quality. First, we model the degradation result from phase to image of optical remote-sensing imaging using Zernike polynomials, thus, a large-scale paired dataset is constructed. Then, a multilevel feature fusion transformer (MFFormer) is introduced to mitigate the defect during restoration. The proposed algorithm incorporates a multilevel feature fusion (MFF) module to fuse feature information from multiscales effectively. Additionally, a multilevel space and frequency loss function is introduced to enhance the learning of high-frequency information to ensure that the edge suppresses noise amplification and ringing effects during recovery. Finally, experimental results on synthetic data show that our method improved by 25.4% and 22.3% with the blurred images on the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) index. Visual results on the GaoFen-1/2A PMS images have enhanced clarity and suppressed artifacts such as ringing which demonstrate the effectiveness and capability of our proposed method.