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

doi:10.3390/s23208442

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	Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory
	C. Wen, T. Nie, M. Li, X. Wang and L. Huang
	2023
发表期刊	Sensors
ISSN	14248220
卷号	23 期号:20
摘要	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. © 2023 by the authors.
DOI	10.3390/s23208442
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收录类别	sci ; ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/68014
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	C. Wen, T. Nie, M. Li, X. Wang and L. Huang. Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory[J]. Sensors,2023,23(20).
APA	C. Wen, T. Nie, M. Li, X. Wang and L. Huang.(2023).Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory.Sensors,23(20).
MLA	C. Wen, T. Nie, M. Li, X. Wang and L. Huang."Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory".Sensors 23.20(2023).

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