Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study.

Yang, Wenhan; Yuan, Ye; Ren, Wenqi; Liu, Jiaying; J Scheirer, Walter; Wang, Zhangyang; Zhang, Taiheng; Zhong, Qiaoyong; Xie, Di; Pu, Shiliang; Zheng, Yuqiang; Qu, Yanyun; Xie, Yuhong; Chen, Liang; Li, Zhonghao; Hong, Chen; Jiang, Hao; Yang, Siyuan; Liu, Yan; Qu, Xiaochao; Wan, Pengfei; Zheng, Shuai; Zhong, Minhui; Su, Taiyi; He, Lingzhi; Guo, Yandong; Zhao, Yao; Zhu, Zhenfeng; Liang, Jinxiu; Wang, Jingwen; Chen, Tianyi; Quan, Yuhui; Xu, Yong; Liu, Bo; Liu, Xin; Sun, Qi; Lin, Tingyu; Li, Xiaochuan; Lu, Feng; Gu, Lin; Zhou, Shengdi; Cao, Cong; Zhang, Shifeng; Chi, Cheng; Zhuang, Chubin; Lei, Zhen; Z Li, Stan; Wang, Shizheng; Liu, Ruizhe; Yi, Dong

doi:10.1109/TIP.2020.2981922

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Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study.

Shared by NobleBlocks on Mar 27, 2020 • 12:00 AM UTC

Authors:

Wenhan Yang

Ye Yuan

Wenqi Ren

Abstract

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. T...

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