RPIR: A Semiblind Unsupervised Learning Image Restoration Method for Optical Synthetic Aperture Imaging Systems With Co-Phase Errors

Optical synthetic aperture imaging (OSAI) systems consist of multiple subapertures arranged in a specific spatial pattern to achieve high resolution imaging comparable to that of a large aperture while reducing costs. However, due to the sparsity of the apertures and co-phase errors, these systems inevitably suffer from image degradation and blurring. Traditional nonblind deconvolution methods are commonly used for image deblurring in OSAI systems, but they require accurate prior knowledge, which, if inaccurate, can affect image restoration quality. Recent deep learning-based methods have achieved remarkable results, but they do not consider the impact of co-phase errors in OSAI systems and require large amounts of real datasets for training. This study proposes a semiblind unsupervised learning method named RPIR for image restoration in OSAI systems with co-phase errors. RPIR is based on the traditional maximum a posteriori (MAP) model and utilizes a multiscale neural network that does not require training to capture the input blur kernel errors, which are then used as the residual prior term of the MAP model. The data term and the prior term are solved using an alternating minimization algorithm. Consequently, RPIR can effectively address the issue of inaccurate blur kernels caused by variations in co-phase errors in OSAI systems. Experimental results demonstrate that RPIR significantly improves image resolution and detail clarity in OSAI systems with complex co-phase errors, outperforming traditional deconvolution methods and other unsupervised deep learning methods.