A Survey on Diffusion Models for Inverse Problems

Autor:	Daras, Giannis, Chung, Hyungjin, Lai, Chieh-Hsin, Mitsufuji, Yuki, Ye, Jong Chul, Milanfar, Peyman, Dimakis, Alexandros G., Delbracio, Mauricio
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples. This has unlocked exciting new possibilities for solving inverse problems, especially in image restoration and reconstruction, by treating diffusion models as unsupervised priors. This survey provides a comprehensive overview of methods that utilize pre-trained diffusion models to solve inverse problems without requiring further training. We introduce taxonomies to categorize these methods based on both the problems they address and the techniques they employ. We analyze the connections between different approaches, offering insights into their practical implementation and highlighting important considerations. We further discuss specific challenges and potential solutions associated with using latent diffusion models for inverse problems. This work aims to be a valuable resource for those interested in learning about the intersection of diffusion models and inverse problems. Comment: Work in progress. 38 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.00083 Zobrazit plný text záznamu View this record from Arxiv