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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Bibliographic Details
Published in:Fractal and fractional Vol. 7; no. 1; p. 70
Main Authors: Yu, Yang, Qin, Chenfeng
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2023
Subjects:
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
Online Access:Get full text
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