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A Survey of Deep Learning-Based Low-Light Image Enhancement
Zhen Tian1,2, Peixin Qu1,2, Jielin Li1,2
1School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China.
Sensors (Basel, Switzerland)
|September 28, 2023
Summary
This paper reviews deep learning methods for low-light image enhancement, addressing issues like poor brightness and noise. It covers network structures, datasets, and evaluation metrics for improving image quality.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Image Processing
Background:
- Low-light images suffer from reduced brightness, contrast, color distortion, and noise.
- Effective low-light image enhancement is crucial for subsequent image processing tasks.
Purpose of the Study:
- To provide a comprehensive review of deep learning-based low-light image enhancement.
- To systematically introduce methods, datasets, and evaluation metrics in this field.
Main Methods:
- Reviewing various deep learning network structures for low-light enhancement.
- Describing low-light image quality evaluation methods.
- Organizing and analyzing low-light image datasets.
Main Results:
- Comparison and analysis of the advantages and disadvantages of existing deep learning methods.
- Identification of key aspects including network architecture, training data, and evaluation metrics.
Conclusions:
- Deep learning offers significant advancements in low-light image enhancement.
- Future research directions are outlined for further development in the field.
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