Deep Unfolded Projected Alternating Minimization Algorithm for Blind Image Super-resolution

Abstract

Blind super-resolution (BSR) has a wide range of applications in fruitful fields, such as pattern recognition, image processing, and signal processing. This problem focuses on recovering the original high-resolution (HR) details and blur kernel from a low-resolution (LR) blurry image. In these years, learning-based BSRs have evolved to become increasingly powerful through more extensive labelled data, better-designed end-to-end model and stronger training algorithm. However, the classic model-based methods typically provide good interpretability for applying prior information and generalizability of dealing with variational factors (blur kernels) with respect to their explicit alternative optimization framework, while the learning-based methods generally highly rely on the training data and are typically perform less effective on the unseen kernels. In this paper, a learning-aided model-based algorithm is proposed to solve the aforementioned issues by unfolding a well-designed model-based BSR approach, the projected alternating minimization (PAM) algorithm, that establishes an explicit alternative optimization iterations with data-driven network-based update module. As a result, the proposed unfolding PAM (UPAM) inherits the good interpretability and generalizability of model-based methods towards solving arbitrary blur kernels, while retaining the high performance of learning-based methods. Extensive experiments verify that the proposed UPAM outperforms both of the model-based and learning-based BSR methods, and gains significantly better performance when dealing with the blur kernels.