Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation

Jingyuan Xia

Abstract:

Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however,
handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a Meta-learning and Markov
Chain Monte Carlo based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is
adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. This
procedure provides an approximation for the rational blur kernel, and introduces a network-level Langevin dynamics into SISR
optimization processes, which contributes to preventing bad local optimal solutions for kernel estimation. Meanwhile, a meta-learning
based alternating optimization procedure is proposed to optimize the kernel generator and image restorer, respectively. In contrast to
the conventional alternating minimization strategy, a meta-learning based framework is applied to learn an adaptive optimization
strategy, which is less-greedy and results in better convergence performance. These two procedures are iteratively processed in a
plug-and-play fashion, for the first time, realizing a learning-based but plug-and-play blind SISR solution in unsupervised inference.
Extensive simulations demonstrate the superior performance and generalization ability of the proposed approach when comparing with
state-of-the-arts on synthesis and real-world datasets.