A Dual-Branch Autoencoder Network for Underwater Low-Light Polarized Image Enhancement

Underwater detection faces uncomfortable illumination conditions, and traditional optical images sensitive to intensity often cannot work well in these conditions. Polarization imaging is a good solution for underwater detection under adverse lighting conditions. However, the process of obtaining po...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 16; no. 7; p. 1134
Main Authors: Xue, Chang, Liu, Qingyu, Huang, Yifan, Cheng, En, Yuan, Fei
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2024
Subjects:
data collection
Datasets
Deep learning
Design
Electromagnetic waves
Feature extraction
Illumination
image analysis
Image enhancement
Image processing
Image quality
learning
Light
Lighting
low-light image enhancement
Luminous intensity
Machine learning
Methods
Neural networks
Noise reduction
Noise sensitivity
Polarization
polarization imaging
remote sensing
solutions
Underwater
Underwater acoustics
underwater image enhancement
Visual tasks
working conditions
China
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Underwater detection faces uncomfortable illumination conditions, and traditional optical images sensitive to intensity often cannot work well in these conditions. Polarization imaging is a good solution for underwater detection under adverse lighting conditions. However, the process of obtaining polarization information causes it to be more sensitive to noise; serious noise reduces the quality of polarized images and subsequent performance in advanced visual tasks. Unfortunately, the flourishing low-light image enhancement methods applied to intensity images have not demonstrated satisfactory performance when transferred to polarized images. In this paper, we propose a low-light image enhancement paradigm based on the antagonistic properties of polarization parameters. Furthermore, we develop a dual-branch network that relies on a gradient residual dense feature extraction module (GRD) designed for polarized image characteristics and polarization loss, effectively avoiding noise introduced during the direct amplification of brightness, and capable of restoring target contour details. To facilitate a data-driven learning method, we propose a simulation method for underwater low-light polarized images. Extensive experimental results on real-world datasets demonstrate the effectiveness of our proposed approach and its superiority against other state-of-the-art methods.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs16071134