Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement-Reference-Cited by-同舟云学术

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

Published:2024-03 Issue:3 Volume:11 Page:708-722
ISSN:2329-9266
Container-title:IEEE/CAA Journal of Automatica Sinica
language:
Short-container-title:IEEE/CAA J. Autom. Sinica

Author:

Liu Hongmin¹^ORCID,Zhang Qi¹^ORCID,Hu Yufan¹^ORCID,Zeng Hui²^ORCID,Fan Bin¹^ORCID

Affiliation:

1. School of Intelligence Science and Technology and the Institute of Artificial Intelligence, University of Science and Technology Beijing,Beijing,China,100083

2. School of Automation and Electrical Engineering, University of Science and Technology Beijing,Beijing,China,100083

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6570654/10433374/10379553.pdf?arnumber=10379553

Reference59 articles.

1. A Survey of Underwater Multi-Robot Systems

2. Mpc-based motion planning and control enables smarter and safer autonomous marine vehicles: perspectives and a tutorial survey

3. High-Resolution Underwater Robotic Vision-Based Mapping and Three-Dimensional Reconstruction for Archaeology

4. What is the Space of Attenuation Coefficients in Underwater Computer Vision?

5. L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion

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