A Reinforcement Learning Paradigm of Configuring Visual Enhancement for Object Detection in Underwater Scenes-Reference-Cited by-同舟云学术

A Reinforcement Learning Paradigm of Configuring Visual Enhancement for Object Detection in Underwater Scenes

Published:2023-04 Issue:2 Volume:48 Page:443-461
ISSN:0364-9059
Container-title:IEEE Journal of Oceanic Engineering
language:
Short-container-title:IEEE J. Oceanic Eng.

Author:

Wang Hao¹^ORCID,Sun Shixin²^ORCID,Bai Xiao³^ORCID,Wang Jian⁴^ORCID,Ren Peng²^ORCID

Affiliation:

1. College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China

2. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China

3. College of Computer Science and Engineering, Beijing Advanced Innovation Center for Big Data and Brain Computing, State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

4. College of Science, China University of Petroleum (East China), Qingdao, China

Funder

Taishan Scholor Project

Qingdao Marine Science and Technology Innovation Project

Innovative Research Team Program for Young Scholars at Universities in Shandong Province

Graduate Innovation Project of China University of Petroleum

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Ocean Engineering

Link

http://xplorestaging.ieee.org/ielx7/48/10102344/10058092.pdf?arnumber=10058092

Reference75 articles.

1. Bayesian retinex underwater image enhancement

2. Perceptual Underwater Image Enhancement With Deep Learning and Physical Priors

3. Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

4. An Underwater Image Enhancement Benchmark Dataset and Beyond

5. UWStereoNet: Unsupervised Learning for Depth Estimation and Color Correction of Underwater Stereo Imagery

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