mlsanityData to 7 Oct 2026

Particle-Filtering-based Latent Diffusion for Inverse Problems

arXiv 2408.13868 ↗First posted 25 Aug 2024cs.CVcited by 5

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Current strategies for solving image-based inverse problems apply latent diffusion models to perform posterior sampling.However, almost all approaches make no explicit attempt to explore the solution space, instead drawing only a single sample from a Gaussian distribution from which to generate their solution. In this paper, we introduce a particle-filtering-based framework for a nonlinear exploration of the solution space in the initial stages of reverse SDE methods. Our proposed particle-filtering-based latent diffusion (PFLD) method and proposed problem formulation and framework can be applied to any diffusion-based solution for linear or nonlinear inverse problems. Our experimental results show that PFLD outperforms the SoTA solver PSLD on the FFHQ-1K and ImageNet-1K datasets on inverse problem tasks of super resolution, Gaussian debluring and inpainting.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 23 of its 47 references that are in the map. First posted on arXiv 25 Aug 2024.

Builds on

23 of 47 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems4 Jun
  2. 2023Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion1 Dec
  3. 2023Prompt-tuning latent diffusion models for inverse problems2 Oct
  4. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models2 Jul
  5. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep

Other references · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  7. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  8. 2022Diffusion Models in Vision: A Surveycited by 1,008
  9. 2022Cold Diffusion: Inverting Arbitrary Image Transforms Without Noisecited by 275
  10. 2022A Survey on Generative Diffusion Modelcited by 238
  11. 2022Diffusion models as plug-and-play priorscited by 230
  12. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 218
6 more
  1. 2023Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervisioncited by 112
  2. 2018Adversarial Regularizers in Inverse Problemscited by 102
  3. 2022Soft Diffusion: Score Matching for General Corruptionscited by 101
  4. 2018Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guaranteescited by 58
  5. 2022On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Modelscited by 31
  6. 2020Deep Convolutional Likelihood Particle Filter for Visual Trackingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Inverse Problem Sampling in Latent Space Using Sequential Monte Carlocited by 7
  2. 2025Inference-Time Scaling of Diffusion Language Models via Trajectory Refinementcited by 7
  3. 2025Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restorationcited by 5
  4. 2026On the Relation of State Space Models and Hidden Markov Modelscited by 2
  5. 2026Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Samplingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Survey on Diffusion Models for Inverse Problems
  2. 2025Diffusion models for inverse problems
  3. 2024Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization
  4. 2024Gaussian is All You Need: A Unified Framework for Solving Inverse Problems via Diffusion Posterior Sampling
  5. 2026Provable diffusion-based posterior sampling for linear inverse problems via DDIM
  6. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
  7. 2025Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance
  8. 2025Unifying and extending Diffusion Models through PDEs for solving Inverse Problems