Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory

Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. Therefore, this paper proposes an effective network based on Retinex for low-illumination image enha...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-10, Vol.23 (20), p.8442
Main Authors: Wen, Chaoran, Nie, Ting, Li, Mingxuan, Wang, Xiaofeng, Huang, Liang
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Decomposition
decomposition network
Deep learning
enhancement network
frequency information
Image processing
low-illumination
Neural networks
reconstruction network
Retinex theory
Teaching methods
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Summary:Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. Therefore, this paper proposes an effective network based on Retinex for low-illumination image enhancement. Inspired by Retinex theory, images are decomposed into two parts in the decomposition network, and sent to the sub-network for processing. The reconstruction network constructs global and local residual convolution blocks to denoize the reflection component. The enhancement network uses frequency information, combined with attention mechanism and residual density network to enhance contrast and improve the details of the illumination component. A large number of experiments on public datasets show that our method is superior to existing methods in both quantitative and visual aspects.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23208442