Extremely Low-light Image Enhancement with Scene Text Restoration
Paper in proceeding, 2022
the scene. In this paper, a novel image enhancement framework is proposed to specifically restore the scene texts, as well as the overall quality of the image simultaneously under extremely low-light images conditions. Particularly, we employed a selfregularised attention map, an edge map, and a novel text detection loss. The quantitative and qualitative experimental results have shown that the proposed model outperforms stateof-the-art methods in terms of image restoration, text detection, and text spotting on See In the Dark and ICDAR15 datasets.
Author
Pohao Hsu
National Tsing Hua University
Che-Tsung Lin
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Chun Chet Ng
University of Malaya
Jie-Long Kew
University of Malaya
Mei Yih Tan
National Tsing Hua University
Shang-Hong Lai
National Tsing Hua University
Chee Seng Chan
University of Malaya
Christopher Zach
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Proceedings - International Conference on Pattern Recognition
10514651 (ISSN)
Vol. 2022-August 317-323978-1-6654-9062-7 (ISBN)
Montréal Québec, Canada,
Subject Categories
Computer Vision and Robotics (Autonomous Systems)
DOI
10.1109/ICPR56361.2022.9956716
ISBN
9781665490627