An End-to-End Underwater-Image-Enhancement Framework Based on Fractional Integral Retinex and Unsupervised Autoencoder

As an essential low-level computer vision task for remotely operated underwater robots and unmanned underwater vehicles to detect and understand the underwater environment, underwater image enhancement is facing challenges of light scattering, absorption, and distortion. Instead of using a specific...

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Vydané v:	Fractal and fractional Ročník 7; číslo 1; s. 70
Hlavní autori:	Yu, Yang, Qin, Chenfeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.01.2023
Predmet:	Algorithms Autonomous underwater vehicles Coders Computer vision Datasets Deep learning Distortion Fractional calculus fractional integral Retinex Haze Image enhancement Machine vision Robots underwater image enhancement Underwater robots unsupervised autoencoder
ISSN:	2504-3110, 2504-3110
On-line prístup:	Získať plný text
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