Diving deeper into underwater image enhancement: A survey
作者：
Highlights：
• We provide a thorough review of the recent techniques.
• We introduce a new taxonomy of the algorithms based on their structural differences.
• A comprehensive analysis is performed based on different architectural aspects.
• We provide a systematic evaluation of algorithms on three publicly available datasets.
• We discuss the challenges and provide insights into possible future directions.
摘要
•We provide a thorough review of the recent techniques.•We introduce a new taxonomy of the algorithms based on their structural differences.•A comprehensive analysis is performed based on different architectural aspects.•We provide a systematic evaluation of algorithms on three publicly available datasets.•We discuss the challenges and provide insights into possible future directions.
论文关键词：Underwater image enhancement,Deep learning,Convolutional neural networks (CNNs),Generative adversarial networks (GANs),Underwater datasets,Underwater evaluation metrics,Survey
论文评审过程：Received 8 January 2020, Revised 22 June 2020, Accepted 8 August 2020, Available online 20 August 2020, Version of Record 25 August 2020.
论文官网地址：https://doi.org/10.1016/j.image.2020.115978
