Image restoration for optical synthetic aperture system via variational physics-informed network

Optical synthetic aperture with homogeneous circular sub-mirrors greatly improves the spatial resolution of space telescopes; however, the discrete and sparse characteristics of the sub-mirrors reduce the mid-frequency modulation transfer function (MTF), resulting in blurred images being obtained. In this paper, a method combining variational physics-informed with deep learning is presented, which shows blind image restoration without complex priors. The constraint effect of traditional maximum a posterior (MAP) framework is removed by variational inference framework, which is embedded into Variational Physics-informed Network (VPIN) to optimize neural network training. Residual dense blocks (RDBs) construction is contributed to image feature extraction. Networks with SSIM-corrected loss functions can be trained at the feature level to help with convergence. When SNR = 30 dB, the PSNR of Golay-6 remote sensing test set increases from 20.16 dB to 23.90 dB, SSIM is from 0.610 to 0.842, and MS-SSIM is from 0.930 to 0.955.