The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
Open accessAbstract
Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. Recent advances in this area are dominated by deep learning-based solutions, where many learning strategies, network structures, loss functions, training data, etc. have been employed. In this paper, we provide a comprehensive survey to cover various aspects ranging from algorithm taxonomy to unsolved open issues. To examine the generalization of existing methods, we propose a low-light image and video dataset, in which the images and videos are taken by different mobile phones' cameras under diverse illumination conditions. Besides, for the first time, we provide a unified online platform that covers many popular LLIE methods, of which the results can be produced through a user-friendly web interface. In addition to qualitative and quantitative evaluation of existing methods on publicly available and our proposed datasets, we also validate their performance in face detection in the dark. This survey together with the proposed dataset and online platform could serve as a reference source for future study and promote the development of this research field. The proposed platform and dataset as well as the collected methods, datasets, and evaluation metrics are publicly available and will be regularly updated. Project page: https://www.mmlab-ntu.com/project/lliv_survey/index.html.
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Cites
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Cited by
Xiao, J., Fu, X., Liu, A., Wu, F., & Zha, Z.-J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183612 · Cited by 389
Dancing in the Dark: A Benchmark towards General Low-light Video Enhancement
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Metadata
- Authors
- Li, C., Guo, C., Han, L., Jiang, J., Cheng, M.-M., Gu, J., & Loy, C. C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-12-01
- Volume
- 44
- Issue
- 12
- Pages
- 9396-9416
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3214264009
- Cited by
- 657
- References
- 98
- FWCI
- 30.69
- Type
- journal article
- Language
- en
- Open access
- green
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- Li, C., Guo, C., Han, L., Jiang, J., Cheng, M.-M., Gu, J., & Loy, C. C. (2022). Low-Light Image and Video Enhancement Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2021.3126387
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