A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution

Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pretraining on labelled datasets. This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pretraining-free learning-based algorithm for solving the BSR problem. DKP can adaptively learn dynamic kernel priors to realize real-time kernel estimation, and thereby enables superior HR image restoration performances. This is achieved by a Markov chain Monte Carlo sampling process on random kernel distributions. The learned kernel prior is then assigned to optimize a blur kernel estimation network, which entails a network-based Langevin dynamic optimization strategy. These two techniques ensure the accuracy of the kernel estimation. DKP can be easily used to replace the kernel estimation models in the existing methods, such as Double-DIP and FKP-DIP, or be added to the off-the-shelf image restoration model, such as diffusion model. In this paper, we incorporate our DKP model with DIP and diffusion model, referring to DIP-DKP and Diff-DKP, for validations. Extensive simulations on Gaussian and motion kernel scenarios demonstrate that the proposed DKP model can significantly improve the kernel estimation with comparable runtime and memory usage, leading to state-of-the-art BSR results. The code is available at https://github.com/XYLGroup/DKP.

1. Introduction

Deep learning provides a new avenue for solving the blind super-resolution (BSR) problem, which aims to reconstruct high-resolution (HR) images from the low-resolution (LR) observations with unknown blur kernels, and is known to be highly non-convex and ill-posed. To alleviate the non-convexity and ill-posedness, most of learning-based BSR methods incorporate image priors via supervised learning based on paired LR-HR samples. However, predefined labeled training datasets are expensive, time-consuming, and even not feasible in specific scenarios, such as for high speed targets (e.g., satellites, aircraft) and medical images (e.g., beating heart). Thus, unsupervised learning-based solutions are highly demanded for BSR problem.