Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

被引:244
作者
Cai, Yuanhao [1 ]
Bian, Hao [1 ]
Lin, Jing [1 ]
Wang, Haoqian [1 ]
Timofte, Radu [2 ]
Zhang, Yulun [3 ]
机构
[1] Tsinghua Univ, Beijing, Peoples R China
[2] Univ Wurzburg, Wurzburg, Germany
[3] Swiss Fed Inst Technol, Zurich, Switzerland
来源
2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023) | 2023年
关键词
D O I
10.1109/ICCV51070.2023.01149
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process. Besides, these methods usually require a tedious multi-stage training pipeline and rely on convolutional neural networks, showing limitations in capturing long-range dependencies. In this paper, we formulate a simple yet principled One-stage Retinex-based Framework (ORF). ORF first estimates the illumination information to light up the low-light image and then restores the corruption to produce the enhanced image. We design an Illumination-Guided Transformer (IGT) that utilizes illumination representations to direct the modeling of non-local interactions of regions with different lighting conditions. By plugging IGT into ORF, we obtain our algorithm, Retinexformer. Comprehensive quantitative and qualitative experiments demonstrate that our Retinexformer significantly outperforms state-of-the-art methods on thirteen benchmarks. The user study and application on low-light object detection also reveal the latent practical values of our method. Code is available at https://github. com/caiyuanhao1998/Retinexformer
引用
收藏
页码:12470 / 12479
页数:10
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