Mid-frequency MTF compensation for optical synthetic aperture based on baseline transform scanning via deep learning

Optical synthetic aperture (OSA) structure of multiple in-phase circular sub-mirrors greatly improves the spatial resolution of space telescopes, but the sparsity and discretization lead to a degradation of mid-frequency response of modulation transfer function (MTF) and loss of image information. A method for improving mid-frequency MTF is presented, named deep learning baseline transform scanning (DLBTS). By performing system baseline transformations, a series of low-resolution imaging sequences containing the missing mid-frequency modulation transfer functions are obtained. Leveraging the powerful feature extraction capability of deep learning, different resolution image sequences are fused to compensate for the missing mid-frequency information. Reverse Regression Module (RRM) is designed to guarantee the loss optimality. When SNR = 30 dB, the PSNR of the Golay-3 image with single-system compensation can be improved from 22.84 dB to 26.29 dB, SSIM can be from 0.685 to 0.765, and MS-SSIM can be raised from 0.865 to 0.916.