GitHub - NadaNour/Underwater-Image-Enhancement-Using-Deep-Learning: The ...
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(PDF) Underwater Image Enhancement Using Deep Learning
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Fast Underwater Image Enhancement For Improved Visual Perception | DeepAI
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 presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms%2C a thorough and systematic review is still absent. To foster future advancements%2C we provide a detailed overview of the UIE task from several perspectives. Firstly%2C we introduce the physical models%2C data construction processes%2C evaluation metrics%2C and loss functions. Secondly%2C we categorize and discuss recent algorithms based on their contributions%2C considering six aspects: network architecture%2C learning strategy%2C learning stage%2C auxiliary tasks%2C domain perspective%2C and disentanglement fusion. Thirdly%2C due to the varying experimental setups in the existing literature%2C a comprehensive and unbiased comparison is currently unavailable. To address this%2C we perform both quantitative and qualitative evaluations of state-of-the-art algorithms across multiple benchmark datasets. Lastly%2C we identify key areas for future research in UIE. A collection of resources for UIE can be found at {https:%2F%2Fgithub.com%2FYuZhao1999%2FUIE}.?resize=650,400 "A Comprehensive Survey On Underwater Image Enhancement Based On Deep ...")
A Comprehensive Survey On Underwater Image Enhancement Based On Deep ...
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An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction
An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction
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 presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms%2C a thorough and systematic review is still absent. To foster future advancements%2C we provide a detailed overview of the UIE task from several perspectives. Firstly%2C we introduce the physical models%2C data construction processes%2C evaluation metrics%2C and loss functions. Secondly%2C we categorize and discuss recent algorithms based on their contributions%2C considering six aspects: network architecture%2C learning strategy%2C learning stage%2C auxiliary tasks%2C domain perspective%2C and disentanglement fusion. Thirdly%2C due to the varying experimental setups in the existing literature%2C a comprehensive and unbiased comparison is currently unavailable. To address this%2C we perform both quantitative and qualitative evaluations of state-of-the-art algorithms across multiple benchmark datasets. Lastly%2C we identify key areas for future research in UIE. A collection of resources for UIE can be found at {https:%2F%2Fgithub.com%2FYuZhao1999%2FUIE}.?resize=91,91 "A Comprehensive Survey On Underwater Image Enhancement Based On Deep ...")
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