Retinex Meets Transformer: Bridging Illumination and Reflectance Maps for Low-Light Image Enhancement

Low-light image enhancement, which is also known as LLIE for short, aims to reconstruct the original normal image from its low-illumination counterpart. Recently, it has received increasingly attention in image restoration. In particular, with the success of deep convolutional neural network (CNN), Retinex-based approaches have emerged as a promising line of research in LLIE, since they can well transfer adequate prior knowledge from an image captured under sufficient illumination to its low-light version for image enhancement. However, existing Retinex-based approaches usually overlook the correlation between Illumination and Reflectance maps which are both derived from the same feature extractor, leading to sub-optimal reconstructed image quality. In this study, we propose a novel Transformer architecture for LLIE, termed Bridging Illumination and Reflectance maps Transformer which is shortly BIRT. It aims to estimate the correlation between Illumination and Reflectance maps derived from Retinex decomposition within a Transformer architecture via the Multi-Head Self-Attention mechanism. In terms of model structure, the proposed BIRT comprises Retinex-based and Transformer-based sub-networks, which allow our model to elevate the image quality by learning cross-feature dependencies and long-range details between Illumination and Reflectance maps. Experimental results demonstrate that the proposed BIRT model achieves competitive performance on par with the state-of-the-arts on the public benchmarking datasets for LLIE.