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Guangcheng Wang
Researcher at Wuhan University
Publications - 26
Citations - 818
Guangcheng Wang is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & Image quality. The author has an hindex of 6, co-authored 15 publications receiving 330 citations. Previous affiliations of Guangcheng Wang include China University of Mining and Technology & Nantong University.
Papers
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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.
Journal ArticleDOI
ATMFN: Adaptive-Threshold-Based Multi-Model Fusion Network for Compressed Face Hallucination
TL;DR: An adaptive-threshold-based multi-model fusion network (ATMFN) for compressed face hallucination, which unifies different deep learning models to take advantages of their respective learning merits.
Journal ArticleDOI
Blind Quality Metric of DIBR-Synthesized Images in the Discrete Wavelet Transform Domain
TL;DR: A novel blind method of DIBR-synthesized images is proposed based on measuring geometric distortion, global sharpness and image complexity and it is shown that the proposed quality method is superior to the competing reference-free state-of-the-art DIBr-syndhesized image quality models.
Proceedings ArticleDOI
When Face Recognition Meets Occlusion: A New Benchmark
Baojin Huang,Zhongyuan Wang,Guangcheng Wang,Kui Jiang,Kangli Zeng,Zhen Han,Xin Tian,Yuhong Yang +7 more
TL;DR: Huang et al. as mentioned in this paper collected a variety of glasses and masks as occlusion, and randomly combined the occlusions attributes (occlusion objects, textures, and colors) to achieve a large number of more realistic occlusive types.