K
Kui Jiang
Researcher at Wuhan University
Publications - 63
Citations - 2088
Kui Jiang is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 13, co-authored 37 publications receiving 851 citations.
Papers
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Proceedings ArticleDOI
Multi-Scale Progressive Fusion Network for Single Image Deraining
TL;DR: This work explores the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi- scale progressive fusion network (MSPFN) for single image rain streak removal.
Journal ArticleDOI
Edge-Enhanced GAN for Remote Sensing Image Superresolution
TL;DR: A generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise is proposed.
Posted Content
Masked Face Recognition Dataset and Application
Zhongyuan Wang,Guangcheng Wang,Baojin Huang,Xiong Zhangyang,Qi Hong,Hao Wu,Peng Yi,Kui Jiang,Nanxi Wang,Yingjiao Pei,Heling Chen,Yu Miao,Zhibing Huang,Jinbi Liang +13 more
TL;DR: A multi-granularity masked face recognition model is developed that achieves 95% accuracy, exceeding the results reported by the industry and is currently the world's largest real-world masked face dataset.
Proceedings ArticleDOI
Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations
TL;DR: This study proposes a novel progressive fusion network for video SR, which is designed to make better use of spatio-temporal information and is proved to be more efficient and effective than the existing direct fusion, slow fusion or 3D convolution strategies.
Journal ArticleDOI
Multi-Memory Convolutional Neural Network for Video Super-Resolution
TL;DR: This paper proposes a multi-memory CNN (MMCNN) for video SR, cascading an optical flow network and an image-reconstruction network that shows superiority over the state-of-the-art methods in terms of PSNR and visual quality and surpasses the best counterpart method by 1 dB at most.