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

This repository is the official PyTorch implementation of MLMC to Blind Super-Resolution

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.
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Requirements

pip install -r requirements.txt

Run MLMC

To run the code without preparing data, run this command:

cd MLMC
python main.py

Run MLMC-USRNet

To run the code without preparing data, run this command:

cd MLMC-USRNet
python main.py

Data Preparation

To prepare testing data, please organize images as data/datasets/Set5/HR/baby.png, and run this command:

cd data
python prepare_dataset.py --model MLMC --sf 2 --dataset Set5

Commonly used datasets can be downloaded here.

Acknowledgement

This project is released under the Apache 2.0 license. The codes are based on FKP and BSRDM. Please also follow their licenses. Thanks for their great works.