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most citedA Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning

9 citations · 9 across the 1 of their papers we have counts for

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cs.CV20269 cited

A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning

Xiaofeng Cong, Yu Zhao, Jie Gui +2

Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic r…

cs.CV2025

The Devil is in the Darkness: Diffusion-Based Nighttime Dehazing Anchored in Brightness Perception

Xiaofeng Cong, Yu-Xin Zhang, Haoran Wei +5

While nighttime image dehazing has been extensively studied, converting nighttime hazy images to daytime-equivalent brightness remains largely unaddressed. Existing methods face tw…

cs.CV2025

Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and Taxonomy

Yu-Xin Zhang, Jie Gui, Baosheng Yu +4

Point cloud registration involves determining a rigid transformation to align a source point cloud with a target point cloud. This alignment is fundamental in applications such as…

cs.CV2024

Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation

Xiaofeng Cong, Jing Zhang, Yeying Jin +5

Underwater images often suffer from quality degradation due to absorption and scattering effects. Most existing underwater image enhancement algorithms produce a single, fixed-colo…

cs.CV2024

A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint

Xiaofeng Cong, Jie Gui, Jing Zhang +2

Existing research based on deep learning has extensively explored the problem of daytime image dehazing. However, few studies have considered the characteristics of nighttime hazy…