Abstract:In the research on low-light image enhancement, although existing technologies make progress in improving image brightness, the issues of insufficient detail restoration and color distortion still persist. To tackle these problems, this study introduces a dual-attention Retinex-based Transformer network—DARFormer. The network consists of an illumination estimation network and corruption restoration network, which aims to enhance the brightness of low light images while preserving more details and preventing color distortion. Illumination estimation network uses an image prior to estimate the brightness mapping, which is used to enhance the brightness of low-light images. The corruption restoration network optimizes the quality of the brightness-enhanced image, employing a Transformer architecture with spatial attention and channel attention. Experiments carried out on public datasets LOL_v1, LOL_v2, and SID show that compared with the prevalent enhancement methods, DARFormer achieves better enhancement results in quantitative and qualitative indicators.