  Deep learning-based methods have achieved significant successes on solving
the blind super-resolution (BSR) problem. However, most of them request
supervised pre-training on labelled datasets. This paper proposes an
unsupervised kernel estimation model, named dynamic kernel prior (DKP), to
realize an unsupervised and pre-training-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.
