HDSR: Image Super-Resolution Method for Harmonic Diffraction Optical Imaging System Based on Plug and Play Technology

Abstract

Harmonic diffractive optical elements (HDOEs) are characterized by their lightweight and compact size, making them promising candidates for applying in future large-aperture space optical imaging systems. However, its lower focusing efficiency and unavoidable manufacturing errors can result in degraded and blurred imaging. To effectively improve the imaging quality of HDOE optical systems, this study proposes an image super-resolution (SR) method based on plug-and-play (PnP) technology, referred to as HDSR. Specifically, the study first establishes the objective function for image SR and then introduces a Poissonian-Gaussian noise model to describe the noise in HDOE optical imaging systems. Based on this, a denoiser based on a convolutional neural network (CNN) is trained and used as the prior term in the optimization function. In addition, the study proposes a learning-based parameter auto-estimation and updating mechanism to reduce the complexity of manually tuning iterative parameters in the PnP technology. In the experimental section, the study explores and verifies the role and importance of the adopted noise model and parameter estimation mechanism. The results show that the proposed HDSR method significantly enhances the imaging quality of the HDOE optical system. In outdoor scenes, the natural image quality evaluator (NIQE) metric average value after SR using this method is 9.28, which is a 49.62% improvement compared to the bicubic method.