Region-Based Unsupervised Low-Light Image Enhancement in the Wild With Explicit Domain Supervision-Reference-Cited by-同舟云学术

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Region-Based Unsupervised Low-Light Image Enhancement in the Wild With Explicit Domain Supervision

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Ma Yingjie¹^ORCID,Xie Shuo¹^ORCID,Xu Wei¹^ORCID,Chen Xinyuan²^ORCID,Huang Xiaolin³^ORCID,Sun Yifei¹^ORCID,Liu Wei³^ORCID

Affiliation:

1. School of Communication and Electronic Engineering, East China Normal University, Shanghai, China

2. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China

3. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

Open Project of the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University

National Key Research Development Project

Shanghai Municipal Science and Technology Research Program

Pujiang Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10574841.pdf?arnumber=10574841

Reference53 articles.

1. Benchmarking Low-Light Image Enhancement and Beyond

2. Low-Light Image and Video Enhancement Using Deep Learning: A Survey

3. Principal Component Analysis-Based Low-Light Image Enhancement Using Reflection Model

4. Camera Response Based Nighttime Image Enhancement Using Concurrent Reflectance

5. HFMNet: Hierarchical Feature Mining Network for Low-Light Image Enhancement

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