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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Vydané v:	Remote sensing (Basel, Switzerland) Ročník 16; číslo 7; s. 1134
Hlavní autori:	Xue, Chang, Liu, Qingyu, Huang, Yifan, Cheng, En, Yuan, Fei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.04.2024
Predmet:	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
On-line prístup:	Získať plný text
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