Hsu, Po-Hao and Lin, Che-Tsung and Ng, Chun Chet and Kew, Jie Long and Tan, Mei Yih and Lai, Shang-Hong and Chan, Chee Seng and Zach, Christopher (2022) Extremely low-light image enhancement with scene text restoration. In: 2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 21-25 August 2022, Montreal.
Full text not available from this repository.Abstract
Deep learning-based methods have made impressive progress in enhancing extremely low-light images - the image quality of the reconstructed images has generally improved. However, we found out that most of these methods could not sufficiently recover the image details, for instance, the texts in the scene. In this paper, a novel image enhancement framework is proposed to precisely restore the scene texts, as well as the overall quality of the image simultaneously under extremely low-light conditions. Mainly, we employed a self-regularised attention map, an edge map, and a novel text detection loss. In addition, leveraging the synthetic low-light images is beneficial for image enhancement on the genuine ones in terms of text detection. The quantitative and qualitative experimental results have shown that the proposed model outperforms state-of-the-art methods in image restoration, text detection, and text spotting on See In the Dark and ICDAR15 datasets.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Low-light image; Enhancement; Scene text restoration |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 13 Feb 2025 04:19 |
| Last Modified: | 13 Feb 2025 04:19 |
| URI: | http://eprints.um.edu.my/id/eprint/40471 |
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