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 (MCMC) 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. The code is available at
this https URL

通过元学习和马尔可夫链蒙特卡罗 (MCMC) 的盲图像超分辨率 (SISR) 方法，从有组织的随机性中学习核先验，同时提出基于元学习的交替优化策略实现一个基于学习的即插即用的盲 SISR 解决方案，通过对合成和真实数据集进行广泛的模拟，表明了该方法具有卓越的性能和泛化能力。