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Chongyi Li
Researcher at Nanyang Technological University
Publications - 89
Citations - 6092
Chongyi Li is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Underwater. The author has an hindex of 22, co-authored 59 publications receiving 2062 citations. Previous affiliations of Chongyi Li include City University of Hong Kong & Tianjin University.
Papers
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Journal ArticleDOI
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Qijian Zhang,Runmin Cong,Chongyi Li,Ming-Ming Cheng,Yuming Fang,Xiaochun Cao,Yao Zhao,Sam Kwong +7 more
TL;DR: An end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs that significantly outperforms the existing state-of-the-art SOD competitors is proposed and a new and challenging optical RSI dataset is constructed that contains 2,000 images with pixel-wise saliency annotations.
Journal ArticleDOI
Hierarchical Features Driven Residual Learning for Depth Map Super-Resolution
TL;DR: A novel deep network for depth map super-resolution (SR), called DepthSR-Net, built on residual U-Net deep network architecture that automatically infers a high-resolution depth map from its low-resolution version by hierarchical features driven residual learning.
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Diving deeper into underwater image enhancement: A survey
TL;DR: In this article, a comprehensive and in-depth survey of deep learning-based underwater image enhancement is provided, which covers various perspectives ranging from algorithms to open issues, and conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark.
Journal ArticleDOI
A hybrid method for underwater image correction
TL;DR: Subjective and objective performance evaluations demonstrate that the proposed method significantly improves both color and visibility of degraded underwater images, and is comparable to and even outperforms several state-of-the-art methods.
Posted Content
An Underwater Image Enhancement Benchmark Dataset and Beyond
TL;DR: The Underwater Image Enhancement Benchmark (UIEB) as mentioned in this paper is a large-scale real-world underwater image enhancement dataset, which consists of 950 realworld underwater images, 890 of which have the corresponding reference images.