An end-to-end deep convolutional neural network for image restoration of sparse aperture imaging system in geostationary orbit

The development of large-aperture telescopes employing monolithic mirrors has been greatly limited by technical constraints and the difficulty of processing and manufacturing. The sparse aperture imaging system employing multiple small-sub apertures arranged and combined onto a co-phasing surface can achieve the equivalent resolution to the fully-filled aperture system, which brings new research ideas for astronomical observation and ground survey. However, the sparsity of apertures will result in blurred imaging. In this paper, we focus on the high-resolution imaging from the geostationary orbit and propose a restoration method for blurred images obtained by the sparse aperture system with a 12-sub-aperture annular-like structure. A SASDeblurNet, containing U-shaped structures and skip connections, is proposed to rapidly restore blurred images end-to-end. MAE, MSE, DSSIM, Charbonnier, and edge loss functions are attempted to train a small amount of data sets in anticipation of better imaging results. The simulation results show that the image restored by the proposed method improves the PSNR by an average of 11 dB and the SSIM of the restoration image improves from 0.77 to 0.94, achieving a high resolution comparable to that of a full-aperture optical system. Compared with traditional non-blind deconvolution algorithms, SASDeblurNet can effectively remove the effect of artifacts. Our work shows that the proposed method has good real-time performance, generalization ability, and noise immunity, which can provide the corresponding data support for on-orbit and real-time observation of sparse aperture imaging systems.