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

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

1. Introduction

Low-light image enhancement is an important yet challenging task in computer vision. It aims to improve the poor visibility and low contrast of low-light images and restore the corruptions (e.g., noise, artifact, color distortion, etc.) hidden in the dark or introduced by the light-up process. These issues challenge not only human visual perception but also other vision tasks like nighttime object detection.