LightingFormer: Transformer-CNN hybrid network for low-light image enhancement

Recent deep-learning methods have shown promising results in low-light image enhancement. However, current methods often suffer from noise and artifacts, and most are based on convolutional neural networks, which have limitations in capturing long-range dependencies resulting in insufficient recovery of extremely dark parts in low-light images. To tackle these issues, this paper proposes a novel Transformer-based low-light image enhancement network called LightingFormer. Specifically, we propose a novel Transformer-CNN hybrid block that captures global and local information via mixed attention. It combines the advantages of the Transformer in capturing long-range dependencies and the advantages of CNNs in extracting low-level features and enhancing locality to recover extremely dark parts and enhance local details in low-light images. Moreover, we adopt the U-Net discriminator to enhance different regions in low-light images adaptively, avoiding overexposure or underexposure, and suppressing noise and artifacts. Extensive experiments show that our method outperforms the state-of-the-art methods quantitatively and qualitatively. Furthermore, the application to object detection demonstrates the potential of our method in high-level vision tasks.