Low-Light Image and Video Enhancement Using Deep Learning: A Survey

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, trai...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2022-12, Vol.44 (12), p.9396-9416
Main Authors: Li, Chongyi, Guo, Chunle, Han, Linghao, Jiang, Jun, Cheng, Ming-Ming, Gu, Jinwei, Loy, Chen Change
Format: Article
Language:English
Subjects:
Algorithms
Cameras
computational photography
Datasets
Deep learning
Face recognition
Feature extraction
Illumination
Image and video restoration
Image enhancement
Lighting
low-light image dataset
low-light image enhancement platform
Machine learning
Photography
Supervised learning
Taxonomy
Training data
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Summary: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 .
ISSN:0162-8828
2160-9292
1939-3539
DOI:10.1109/TPAMI.2021.3126387