Low-Light Image Enhancement with Multi-stage Residue Quantization and Brightness-aware Attention

Low-light image enhancement (LLIE) aims to recover illumination and improve the visibility of low-light images. Conventional LLIE methods often produce poor results because they neglect the effect of noise interference. Deep learning-based LLIE methods focus on learning a mapping function between low-light images and normal-light images that outperforms conventional LLIE methods. However, most deep learning-based LLIE methods cannot yet fully exploit the guidance of auxiliary priors provided by normal-light images in the training dataset. In this paper, we propose a brightness-aware network with normal-light priors based on brightness-aware attention and residual-quantized codebook. To achieve a more natural and realistic enhancement, we design a query module to obtain more reliable normal-light features and fuse them with low-light features by a fusion branch. In addition, we propose a brightness-aware attention module to further improve the robustness of the network to the brightness. Extensive experimental results on both real-captured and synthetic data show that our method outperforms existing state-of-the-art methods.