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Sampling Strategies in Bayesian Inversion: A Study of RTO and Langevin Methods

arXiv 2406.16658 ↗First posted 24 Jun 2024eess.IV cs.CV math.STcited by 0

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Abstract (arXiv)

This paper studies two classes of sampling methods for the solution of inverse problems, namely Randomize-Then-Optimize (RTO), which is rooted in sensitivity analysis, and Langevin methods, which are rooted in the Bayesian framework. The two classes of methods correspond to different assumptions and yield samples from different target distributions. We highlight the main conceptual and theoretical differences between the two approaches and compare them from a practical point of view by tackling two classical inverse problems in imaging: deblurring and inpainting. We show that the choice of the sampling method has a significant impact on the quality of the reconstruction and that the RTO method is more robust to the choice of the parameters.

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References · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015Variational Inference with Normalizing Flowscited by 2,161
  3. 2019An Introduction to Variational Autoencoderscited by 997
  4. 2014Stochastic Gradient Hamiltonian Monte Carlocited by 341
  5. 2015Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithmcited by 195
  6. 2021Bayesian imaging using Plug & Play priors: when Langevin meets Tweediecited by 96
  7. 2016Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreaucited by 83
  8. 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
  9. 2020Learning Maximally Monotone Operators for Image Recoverycited by 62
  10. 2018Split-and-augmented Gibbs sampler - Application to large-scale inference problemscited by 47
  11. 2013Proximal Markov chain Monte Carlo algorithmscited by 46
  12. 2021Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithmscited by 24
4 more
  1. 2018Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimizationcited by 22
  2. 2022The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distributioncited by 11
  3. 2017A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imagingcited by 7
  4. 2018Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imagingcited by 5

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