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

• URL: http://arxiv.org/abs/2406.08896v1
• Date: Thu, 13 Jun 2024 07:50:15 GMT
• Title: Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
• Authors: Jingyuan Xia, Zhixiong Yang, Shengxi Li, Shuanghui Zhang, Yaowen Fu, Deniz Gündüz, Xiang Li,
• Abstract summary: We propose a Meta-learning and Markov Chain Monte Carlo based SISR approach to learn kernel priors from organized randomness. A lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. A meta-learning-based alternating optimization procedure is proposed to optimize the kernel generator and image restorer.
• Score: 46.5310645609264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (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 https://github.com/XYLGroup/MLMC.

Related papers

• Neural-Kernel Conditional Mean Embeddings [26.862984140099837]
  Kernel conditional mean embeddings (CMEs) offer a powerful framework for representing conditional distribution, but they often face scalability and challenges. We propose a new method that effectively combines the strengths of deep learning with CMEs in order to address these challenges. In conditional density estimation tasks, our NN-CME hybrid achieves competitive performance and often surpasses existing deep learning-based methods.
  arXiv  Detail & Related papers  (2024-03-16T08:51:02Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel [22.23550794664218]
  This paper presents a novel GP linear multiple kernel (LMK) and a generic sparsity-aware distributed learning framework. The framework incorporates a quantized alternating direction method of multipliers (ADMM) for collaborative learning among multiple agents. Experiments on diverse datasets demonstrate the superior prediction performance and efficiency of our proposed methods.
  arXiv  Detail & Related papers  (2023-09-15T07:05:33Z)
• Online Network Source Optimization with Graph-Kernel MAB [62.6067511147939]
  We propose Grab-UCB, a graph- kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks. We describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy.
  arXiv  Detail & Related papers  (2023-07-07T15:03:42Z)
• Scalable Optimal Margin Distribution Machine [50.281535710689795]
  Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the novel margin theory. This paper proposes a scalable ODM, which can achieve nearly ten times speedup compared to the original ODM training method.
  arXiv  Detail & Related papers  (2023-05-08T16:34:04Z)
• CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning [50.90384817689249]
  CoopInit is a cooperative learning-based strategy that can quickly learn a good starting point for GANs. We demonstrate the effectiveness of the proposed approach on image generation and one-sided unpaired image-to-image translation tasks.
  arXiv  Detail & Related papers  (2023-03-21T07:49:32Z)
• Meta-Learning with Neural Tangent Kernels [58.06951624702086]
  We propose the first meta-learning paradigm in the Reproducing Kernel Hilbert Space (RKHS) induced by the meta-model's Neural Tangent Kernel (NTK) Within this paradigm, we introduce two meta-learning algorithms, which no longer need a sub-optimal iterative inner-loop adaptation as in the MAML framework. We achieve this goal by 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS; and 2) solving the adaptation analytically based on the NTK theory.
  arXiv  Detail & Related papers  (2021-02-07T20:53:23Z)
• End-to-end Kernel Learning via Generative Random Fourier Features [31.57596752889935]
  Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. In this paper, we consider a one-stage process that incorporates the kernel learning and linear learner into a unifying framework.
  arXiv  Detail & Related papers  (2020-09-10T00:27:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.