Unsupervised Low-Light Image Enhancement via Luminance Mask and Luminance-Independent Representation Decoupling-Reference-Cited by-同舟云学术

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Unsupervised Low-Light Image Enhancement via Luminance Mask and Luminance-Independent Representation Decoupling

Published:2024-08 Issue:4 Volume:8 Page:3029-3039
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Peng Bo¹^ORCID,Zhang Jia¹^ORCID,Zhang Zhe¹^ORCID,Huang Qingming²^ORCID,Chen Liqun³^ORCID,Lei Jianjun¹^ORCID

Affiliation:

1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

3. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China

Funder

National Natural Science Foundation of China

Science and Technology Major Project of Tibetan Autonomous Region of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10607834/10466480.pdf?arnumber=10466480

Reference44 articles.

1. Optimized Bezier Curve Based Intensity Mapping Scheme for Low Light Image Enhancement

2. Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation

3. Toward Fast, Flexible, and Robust Low-Light Image Enhancement

4. APNet: Adversarial Learning Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection

5. Parameter-Free Similarity-Aware Attention Module for Medical Image Classification and Segmentation

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