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Xiaoguang Mei
Researcher at Wuhan University
Publications - 75
Citations - 1984
Xiaoguang Mei is an academic researcher from Wuhan University. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 14, co-authored 60 publications receiving 920 citations. Previous affiliations of Xiaoguang Mei include Huazhong University of Science and Technology & Central China Normal University.
Papers
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DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion
TL;DR: A new end-to-end model, termed as dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions, which establishes an adversarial game between a generator and two discriminators.
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Infrared and visible image fusion based on target-enhanced multiscale transform decomposition
TL;DR: Qualitative and quantitative experimental results on publicly available datasets demonstrate that the proposed target-enhanced multiscale transform (MST) decomposition model for infrared and visible image fusion can generate fused images with clearly highlighted targets and abundant details.
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Spectral-Spatial Attention Networks for Hyperspectral Image Classification
Xiaoguang Mei,Erting Pan,Yong Ma,Xiaobing Dai,Jun Huang,Fan Fan,Qinglei Du,Hong Zheng,Jiayi Ma +8 more
TL;DR: Experimental results demonstrate that the proposed spectral-spatial attention network for hyperspectral image classification can fully utilize the spectral and spatial information to obtain competitive performance.
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Hyperspectral image denoising with superpixel segmentation and low-rank representation
TL;DR: This paper integrates superpixel segmentation (SS) into LRR and proposes a novel denoising method called SSLRR, which excavate the spatial-spectral information of HSI by combining PCA with SS, and is better than simply dividing the HSI into square patches.
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An Infrared Small Target Detecting Algorithm Based on Human Visual System
TL;DR: The difference of Gabor (DoGb) filters is proposed and improved (IDoGb), which is an extension of DoG but is sensitive to orientations and can better suppress the complex background edges, then achieves a lower false alarm rate.