Underwater Image Enhancement Network Based on Dual Layers Regression
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Published:2024-01-02
Issue:1
Volume:13
Page:196
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Jia Huidi123ORCID, Xiao Yeqing12, Wang Qiang14, Chen Xiai12ORCID, Han Zhi12ORCID, Tang Yandong12
Affiliation:
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 3. University of Chinese Academy of Sciences, Beijing 100049, China 4. Key Laboratory of Manufacturing Industrial Integrated, Shenyang University, Shenyang 110044, China
Abstract
Due to the absorption and scattering of light in water, captured underwater images often suffer from some degradation, such as color cast, blur, and low contrast. These types of degradation usually affect and degrade the performance of computer vision methods and tasks under water. In order to solve these problems, in this paper, we propose a multi-stage and gradually optimized underwater image enhancement deep network, named DLRNet, based on dual layers regression. Our network emphasizes important information by aggregating different depth features in the channel attention module, and the dual-layer regression module is designed with regression to obtain the ambient light and scene light transmission for an underwater image. Then, with the underwater imaging model, the enhanced underwater image for a degraded image can be obtained with normal color, higher clarity, and contrast. The experiments on some different datasets with qualitative analysis and quantitative evaluations validate our network, and show that it outperforms some state-of-the-art approaches.
Funder
National Natural Science Foundation of China Natural Science Foundation of Liaoning Province of China Youth Innovation Promotion Association of the Chinese Academy of Sciences National Science Foundation of Liaoning Province
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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