Perturb-and-Solve: Efficient Learned-Operator Conditioning for Latent Diffusion Inverse Problems
cs.LG, cs.CV
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
Sources
- Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
- A Survey on Diffusion Models for Inverse Problems
- Provable diffusion-based posterior sampling for linear inverse problems via DDIM
- FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems
- PnP-Flow: Plug-and-Play Image Restoration with Flow Matching
- Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
- Steering Rectified Flow Models in the Vector Field for Controlled Image Generation
- Sparse Scheduled Diffusion Guidance for Inverse Problems
- Consistency Regularised Gradient Flows for Inverse Problems
- Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
- Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing
- Denoising Diffusion Models for Plug-and-Play Image Restoration
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