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474 results on '"Wang, Shengchun"'

3. Improved Real-time Image Smoothing with Weak Structures Preserved and High-contrast Details Removed

Author

Wang, Shengchun, Wang, Wencheng, and Hou, Fei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Image smoothing is by reducing pixel-wise gradients to smooth out details. As existing methods always rely on gradients to determine smoothing manners, it is difficult to distinguish structures and details to handle distinctively due to the overlapped ranges of gradients for structures and details. Thus, it is still challenging to achieve high-quality results, especially on preserving weak structures and removing high-contrast details. In this paper, we address this challenge by improving the real-time optimization-based method via iterative least squares (called ILS). We observe that 1) ILS uses gradients as the independent variable in its penalty function for determining smoothing manners, and 2) the framework of ILS can still work for image smoothing when we use some values instead of gradients in the penalty function. Thus, corresponding to the properties of pixels on structures or not, we compute some values to use in the penalty function to determine smoothing manners, and so we can handle structures and details distinctively, no matter whether their gradients are high or low. As a result, we can conveniently remove high-contrast details while preserving weak structures. Moreover, such values can be adjusted to accelerate optimization computation, so that we can use fewer iterations than the original ILS method for efficiency. This also reduces the changes onto structures to help structure preservation. Experimental results show our advantages over existing methods on efficiency and quality.

Published
2023

4. Unpaired Image Dehazing for Real Hazy Images

Author

Zhao, Hongwei, Pei, Yanting, Jin, Yi, Huang, Yaping, Wang, Shengchun, Li, Yidong, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhang, Min, editor, Xu, Bin, editor, Hu, Fuyuan, editor, Lin, Junyu, editor, Song, Xianhua, editor, and Lu, Zeguang, editor

Published
2024
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18. Radar Fusion Monocular Depth Estimation Based on Dual Attention

Author

Long, JianYu, Huang, JinGui, Wang, ShengChun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Xingming, editor, Zhang, Xiaorui, editor, and Xia, Zhihua, editor

Published
2022
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19. Thunderstorm Recognition Based on Neural Network PRDsNET Models

Author

Wang, ShengChun, Hu, DanYi, Zhou, ChangQing, Xu, JingYu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qiu, Meikang, editor, Gai, Keke, editor, and Qiu, Han, editor

Published
2022
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26. From Digital Model to Reality Application: A Domain Adaptation Method for Rail Defect Detection

Author

Cui, Wenkai, Wang, Jianzhu, Yu, Haomin, Peng, Wenjuan, Wang, Le, Wang, Shengchun, Dai, Peng, Li, Qingyong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ma, Huimin, editor, Wang, Liang, editor, Zhang, Changshui, editor, Wu, Fei, editor, Tan, Tieniu, editor, Wang, Yaonan, editor, Lai, Jianhuang, editor, and Zhao, Yao, editor

Published
2021
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28. Para-selective nitrobenzene amination lead by C(sp2)-H/N-H oxidative cross-coupling through aminyl radical.

Author

Zhang, Zhen, Yue, Shusheng, Jin, Bo, Yang, Ruchun, Wang, Shengchun, Zhang, Tianqi, Sun, Li, Lei, Aiwen, and Cai, Hu

Subjects
RADICALS (Chemistry), AMINATION, NITROBENZENE, ALIPHATIC amines, AROMATIC amines, NITROAROMATIC compounds
Abstract

Arylamines, serving as crucial building blocks in natural products and finding applications in multifunctional materials, are synthesized on a large scale via an electrophilic nitration/reduction sequence. However, the current methods for aromatic C–H amination have not yet attained the same level of versatility as electrophilic nitration. Here we show an extensively investigated transition metal-free and regioselective strategy for the amination of nitrobenzenes, enabling the synthesis of 4-nitro-N-arylamines through C(sp2)-H/N-H cross-coupling between electron-deficient nitroarenes and amines. Mechanistic studies have elucidated that the crucial aspects of these reactions encompass the generation of nitrogen radicals and recombination of nitrobenzene complex radicals. The C(sp2)-N bond formation is demonstrated to be highly effective for primary and secondary arylamines as well as aliphatic amines under mild conditions, exhibiting exceptional tolerance towards diverse functional groups in both nitroarenes and amines (>100 examples with yields up to 96%). Notably, this C(sp2)-H/N-H cross-coupling exhibits exclusive para-selectivity. Direct radical C–H amination strategies have exhibited innovation, but challenges remain with C–H amination of electron-poor nitroarenes due to the essence of the electron-deficient nitrogen radical. Herein, the authors report a transition metal-free dehydrogenative C(sp2)-H/N-H cross-coupling between electron-poor nitroarenes and amines. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Chinese guidelines on the clinical application of 5-aminolevulinic acid-based photodynamic therapy in dermatology (2021 edition)

Author

Shi, Lei, Wang, Hongwei, Chen, Kun, Yan, Jia, Yu, Bo, Wang, Shengchun, Yin, Rui, Nong, Xiang, Zou, Xianbiao, Chen, Zhou, Li, Chengxin, Chen, Liuqing, Zhang, Chunlei, Zhang, Furen, Zheng, Heyi, Zheng, Min, Tu, Ping, Xu, Jinhua, Tao, Juan, Kang, Xiaojing, Zeng, Kang, Lu, Yan, Yu, Nan, Lei, Xia, Pan, Meng, Ju, Qiang, Gu, Heng, and Wang, Xiuli

Published
2021
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32. Research on DINA Model in Online Education

Author

Liu, Jia, Tang, Wensheng, He, Xuping, Yang, Bo, Wang, Shengchun, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Liu, Shuai, editor, Sun, Guanglu, editor, and Fu, Weina, editor

Published
2020
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33. Research on Educational Data Mining Based on Big Data

Author

He, Xuping, Tang, Wensheng, Liu, Jia, Yang, Bo, Wang, Shengchun, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Liu, Shuai, editor, Sun, Guanglu, editor, and Fu, Weina, editor

Published
2020
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34. Precipitation Phase Inversion Algorithm for Extremely Unbalanced Data

Author

Wang, Shengchun, Jiang, Chengcheng, Huang, Jingui, Xu, Jinyu, Deng, Hong, Huang, Long, Yu, Xiaozhong, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Xingming, editor, Wang, Jinwei, editor, and Bertino, Elisa, editor

Published
2020
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36. A semi-supervised video dehazing method based on CNNs

Author

WANG, Fan, primary, MA, Weixin, additional, WANG, Le, additional, DAI, Peng, additional, LIU, Junbo, additional, LI, Hailang, additional, ZHAO, Xinxin, additional, WANG, Ning, additional, FANG, Yue, additional, WANG, Shengchun, additional, and Pei, Yanting, additional

Published
2023
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38. Synthetic‐to‐realistic domain adaptation for cold‐start of rail inspection systems.

Author

Huang, Qilong, Wang, Jianzhu, Song, Yixiao, Cui, Wenkai, Li, Hailang, Wang, Shengchun, Dai, Peng, Zhao, Xinxin, and Li, Qingyong

Subjects
DEEP learning, SURFACE defects, INSPECTION & review, IMAGE converters, NEW business enterprises
Abstract

Rail surface defects are potential danger factors for railway systems, and visual inspection of surface defects plays a vital role in rail maintenance. Recently, the methods based on deep learning have been widely used in rail inspection systems, but such systems often face the problem of a lack of defect samples for training deep learning models at start‐up, which is called the cold‐start problem. It is challenging to obtain sufficient defect samples since defects are sparse and even non‐existent for a running railway system. Therefore, a synthetic‐to‐realistic domain adaptation (SRDA) method is proposed for real‐world rail inspection. SRDA adapts synthetic images to look more realistic for reducing the domain gap between synthetic images and realistic rail surface images and obtains translated images that consist of synthetic defect information and realistic rail background. After that, the translated images are used to train a detector for rail inspection of rail surface defects. In order to make the detector more robust to complex backgrounds, SRDA generates images with the same defect‐level semantics but with different texture appearances and makes the detector align these images in the learned feature space. In addition, the synthetic and realistic rail surface defects (SRRSD) dataset containing 20,662 images is built. The experimental results on SRRSD show that SRDA achieves higher detection performance than other established domain adaption methods with 19.0% for F1score${\mathrm{F}}1\ {\mathrm{score}}\ $ and 26.7% for average precision. [ABSTRACT FROM AUTHOR]

Published
2024
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