arXiv AI By Yang Zheng, Jiahua Liu, Tongyao Pang, Wen Li, Zhaoqiang Liu

Outlier-Robust Diffusion Solvers for Inverse Problems

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Computer Vision
Sep 2

Diffusion Based Unpaired Data Learning for Inverse Problems

The paper introduces LUD-DIF, a diffusion-based method that solves inverse problems using unpaired data. By deriving the evidence lower bound of the joint distribution and decoupling it into two independent diffusion processes under a weak‑coupling assumption, the authors provide a variational inference framework, a loss function, and an error‑bound analysis. Experiments show that LUD‑DIF performs well across multiple image inverse problems, demonstrating its effectiveness and generalization in unpaired settings.

By Chenglong Bao, Yiming Dang, Chenguang Duan, Yuling Jiao, Defeng Sun
arXiv Computer Vision
Oct 2

Principled Design of Diffusion-based Optimizers for Inverse Problems

Score‑based diffusion models excel at inverse problems but suffer from slow inference and task‑specific hyperparameter tuning. This paper introduces principled reparameterizations that create invariances, enabling the reuse of the same hyperparameters across different tasks. Building on the RED‑diff framework, the authors present OptDiff, a streamlined pipeline that integrates convex optimization to accelerate inference, achieving notable speedups and higher image quality in reconstruction, deblurring, and super‑resolution experiments.

By Julio Oscanoa, Irmak Sivgin, Cagan Alkan, Daniel Ennis, John Pauly, Mert Pilanci, Shreyas Vasanawala