Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory
文献类型：期刊论文
| 作者 | C. Wen, T. Nie, M. Li, X. Wang and L. Huang |
| 刊名 | Sensors
 |
| 出版日期 | 2023 |
| 卷号 | 23期号:20 |
| ISSN号 | 14248220 |
| DOI | 10.3390/s23208442 |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| 源URL | [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). |
入库方式： OAI收割
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