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, models, and results are available at https://github.com/caiyuanhao1998/Retinexformer
@article{arxiv.2303.06705,
title = {Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement},
author = {Yuanhao Cai and Hao Bian and Jing Lin and Haoqian Wang and Radu Timofte and Yulun Zhang},
journal= {arXiv preprint arXiv:2303.06705},
year = {2023}
}
Comments
ICCV 2023; The first Transformer-based method for low-light image enhancement