| Summary: | Optical sparse aperture (OSA) systems show great potential for the next generation astronomical telescope system due to its excellent high resolution with low volume and weight. However, the sparse arrangement causes its mid-frequency modulation transfer function to be lower compared with a single fully-filled aperture system, which further leads to blurred images and reduced contrast. Therefore, image restoration becomes an indispensable part for OSA systems. In this paper, a dual target network (DTN) is proposed for the image restoration of OSA systems. The noise in a raw image is estimated with interpolation and difference calculation. A block matching 3D filter is used as a denoiser. A denoised image is regarded as a degraded image which cannot be accurately modeled. To cope with the restoration problem, a dual target (negative structural similarity and the sum of fidelity and regularization term) network is trained. A function determined by the filling factor and the aperture distribution is trained as a correction term of the network. The trained network is used to deconvolve the denoised image. Simulation and experiment results show that the proposed method has good peak signal-to-noise ratio and structure similarity. For a Golay-6 system with a filling factor of 0.3245, when the signal-to-noise ratio is 30 dB, the DTN method increases the average peak signal to noise ratio from 22.6 dB to 31.7 dB and improves the average structural similarity from 0.77 to 0.90.
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