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

Outlier-Robust Diffusion Solvers for Inverse Problems

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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