RestoreNet: a deep learning framework for image restoration in optical synthetic aperture imaging system

Abstract Imaging blur is an inevitable problem in optical synthetic aperture imaging system because of the low response of frequency. Non-blind deconvolution algorithms are usually used for image restoration to obtain clear and high-resolution images. However, accurate prior information on the optical transfer function of system is required in the non-blind methods. As a data-driven approach, recent developments in deep learning have shown great potential in image processing. In this paper, a U-shaped deep learning framework named RestoreNet is proposed for image restoration, especially for removing the blur of optical synthetic aperture imaging system in a blind way. Numerical simulation and experiment results show that RestoreNet is an effective alternative with great restoration ability, stability and generalization in the image restoration of optical synthetic aperture imaging system.