Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications-Reference-Cited by-同舟云学术

Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications

Published:2023-01 Issue:1 Volume:40 Page:148-163
ISSN:1053-5888
Container-title:IEEE Signal Processing Magazine
language:
Short-container-title:IEEE Signal Process. Mag.

Author:

Zhao Zhizhen¹^ORCID,Ye Jong Chul²^ORCID,Bresler Yoram¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, USA

2. Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejon, Korea

Funder

National Science Foundation

U.S. Army

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/79/10004746/10004774-aam.pdf

Reference58 articles.

1. Denoising diffusion implicit models;song;Proc 9th Int Conf Learn Represent (ICLR),0

2. Generative Modeling Using the Sliced Wasserstein Distance

3. Implicit generation and modeling with energy-based models;du;Proc 33rd Adv Neural Inf Process Syst,0

4. Subspace robust Wasserstein distances;paty;Proc 36th Int Conf Mach Learn,0

5. Denoising diffusion probabilistic models;ho;Proc Adv Neural Inf Process Syst,0

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