368 Papers
929 Citations
Liang Xiao is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Computer science & Hyperspectral imaging. The author has an hindex of 24, co-authored 277 publications. Previous affiliations of Liang Xiao include Royal College of Surgeons in Ireland & Hubei University of Technology.
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Papers
Comments on Staircase effect alleviation by coupling gradient fidelity term
Liang Xiao,Lili Huang,Zhihui Wei +2 more
TL;DR: This work proposes a new theoretical analysis of the alleviation staircase effect using a coupling gradient fidelity term that helps to alleviate the staircase effect to some extent.
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Bipartite Residual Network for Change Detection in Heterogeneous Optical and Radar Images
Haocheng Zhang,Jia Liu,Liang Xiao +2 more
- 26 Sep 2020
TL;DR: In this paper, a bipartite unsupervised residual networks (ResNet) was proposed for change detection based on two heterogeneous images acquired by optical and radars sensors on different dates.
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Reliability and validity of prognostic indicators for Guillain–Barré syndrome in children
TL;DR: In Chinese paediatric patients with GBS who were admitted within 2 weeks after disease onset, the mEGOS and EGOS are validated indicators for the prediction of clinical outcomes 6 months after onset and EGRIS is helpful in predicting the implementation of mechanical ventilation in the acute phase.
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Brønsted Acid Promoted Sulfonylation of Propargylic Alcohols: Synthesis of Triaryl Allenyl Sulfones under Mild Conditions
Shimin Jiang,Shan-Tao Du,Ruchun Yang,Fengyan Jin,Zhao-Zhao Zhou,Wan-Fa Tian,Xian-Rong Song,Liang Xiao +7 more
TL;DR: In this paper , an efficient and practical approach for the synthesis of triaryl allenyl sulfones from easily accessible propargylic alcohols was developed, by using sodium sulfinates as a sulfonyl source in the presence of Brønsted acid.
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Fractional-order iterative regularization method for total variation based image denoising
Jun Zhang,Zhihui Wei,Liang Xiao +2 more
TL;DR: An experience method to choose the order of derivative adaptively for the partly textured images to improve the performance of noise removal and texture preservation and the adaptive method has low computational cost and can improve the result visually efficiently.
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