Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Dejia Xu"'
Publikováno v:
Atmosphere, Vol 13, Iss 10, p 1654 (2022)
Based on the daily data of 24 meteorological warfare points in Ningxia, the potential evaporation (ET0), the Mann–Kendall test (MK), and inverse distance weight interpolation were used to analyze the temporal and spatial changes in the potential ev
Externí odkaz:
https://doaj.org/article/cc9ed30261374c2d9a0196738eb5a6fa
Autor:
Donglin Gan, Ziqiang Huang, Xiao Wang, Dejia Xu, Shuquan Rao, Kefeng Wang, Fuzeng Ren, Lili Jiang, Chaoming Xie, Xiong Lu
Publikováno v:
Materials Horizons.
Core–shell PEDOT@PZIF-71 nanoparticles as conductive nanofillers were homogeneously incorporated into a hydrophilic network to form conductive hydrogels for bio-electronics and bio-capacitors.
Publikováno v:
Proceedings of the 30th ACM International Conference on Multimedia.
Publikováno v:
Proceedings of the 30th ACM International Conference on Multimedia.
Autor:
Peng Jiang1 Jiangpeng3867@126.com, Lei Li1,2 lilei867@163.com, Dejia Xu3 529000230@qq.com, Rui Wang3 amwangrui@126.com, Quan Sun3 sqnxu@sina.com
Publikováno v:
International Journal of Agricultural & Biological Engineering. Apr2024, Vol. 17 Issue 2, p186-192. 7p.
Publikováno v:
International Journal of Computer Vision. 129:1153-1184
In this paper, we present a systematic review and evaluation of existing single-image low-light enhancement algorithms. Besides the commonly used low-level vision oriented evaluations, we additionally consider measuring machine vision performance in
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200465
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa076171b4e147141141d1303969320a
https://doi.org/10.1007/978-3-031-20047-2_42
https://doi.org/10.1007/978-3-031-20047-2_42
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031197833
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::748f3855486d52aeca9b56b0b55d7fd1
https://doi.org/10.1007/978-3-031-19784-0_37
https://doi.org/10.1007/978-3-031-19784-0_37
Publikováno v:
AAAI
Data-driven rain streak removal methods, which most of rely on synthesized paired data, usually come across the generalization problem when being applied in real cases. In this paper, we propose a novel deep-learning based rain streak removal method
Publikováno v:
IEEE Transactions on Multimedia. :1-1