Text in the Dark: Extremely Low-Light Text Image Enhancement
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arXiv
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| Format: | Preprint |
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2024
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| author | Lin, Che-Tsung Ng, Chun Chet Tan, Zhi Qin Nah, Wan Jun Wang, Xinyu Kew, Jie Long Hsu, Pohao Lai, Shang Hong Chan, Chee Seng Zach, Christopher |
| author_facet | Lin, Che-Tsung Ng, Chun Chet Tan, Zhi Qin Nah, Wan Jun Wang, Xinyu Kew, Jie Long Hsu, Pohao Lai, Shang Hong Chan, Chee Seng Zach, Christopher |
| contents | Extremely low-light text images are common in natural scenes, making scene text detection and recognition challenging. One solution is to enhance these images using low-light image enhancement methods before text extraction. However, previous methods often do not try to particularly address the significance of low-level features, which are crucial for optimal performance on downstream scene text tasks. Further research is also hindered by the lack of extremely low-light text datasets. To address these limitations, we propose a novel encoder-decoder framework with an edge-aware attention module to focus on scene text regions during enhancement. Our proposed method uses novel text detection and edge reconstruction losses to emphasize low-level scene text features, leading to successful text extraction. Additionally, we present a Supervised Deep Curve Estimation (Supervised-DCE) model to synthesize extremely low-light images based on publicly available scene text datasets such as ICDAR15 (IC15). We also labeled texts in the extremely low-light See In the Dark (SID) and ordinary LOw-Light (LOL) datasets to allow for objective assessment of extremely low-light image enhancement through scene text tasks. Extensive experiments show that our model outperforms state-of-the-art methods in terms of both image quality and scene text metrics on the widely-used LOL, SID, and synthetic IC15 datasets. Code and dataset will be released publicly at https://github.com/chunchet-ng/Text-in-the-Dark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14135 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Text in the Dark: Extremely Low-Light Text Image Enhancement Lin, Che-Tsung Ng, Chun Chet Tan, Zhi Qin Nah, Wan Jun Wang, Xinyu Kew, Jie Long Hsu, Pohao Lai, Shang Hong Chan, Chee Seng Zach, Christopher Computer Vision and Pattern Recognition Extremely low-light text images are common in natural scenes, making scene text detection and recognition challenging. One solution is to enhance these images using low-light image enhancement methods before text extraction. However, previous methods often do not try to particularly address the significance of low-level features, which are crucial for optimal performance on downstream scene text tasks. Further research is also hindered by the lack of extremely low-light text datasets. To address these limitations, we propose a novel encoder-decoder framework with an edge-aware attention module to focus on scene text regions during enhancement. Our proposed method uses novel text detection and edge reconstruction losses to emphasize low-level scene text features, leading to successful text extraction. Additionally, we present a Supervised Deep Curve Estimation (Supervised-DCE) model to synthesize extremely low-light images based on publicly available scene text datasets such as ICDAR15 (IC15). We also labeled texts in the extremely low-light See In the Dark (SID) and ordinary LOw-Light (LOL) datasets to allow for objective assessment of extremely low-light image enhancement through scene text tasks. Extensive experiments show that our model outperforms state-of-the-art methods in terms of both image quality and scene text metrics on the widely-used LOL, SID, and synthetic IC15 datasets. Code and dataset will be released publicly at https://github.com/chunchet-ng/Text-in-the-Dark. |
| title | Text in the Dark: Extremely Low-Light Text Image Enhancement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.14135 |