Integration-And-Diffusion Network For Low-Light Image Enhancement

Images captured under extreme low-light conditions often suffer from low Signal-to-Noise Ratio(SNR) caused by low photon count, making low-light image enhancement challenging. Deep learning-based methods have recently yielded impressive progress by reconstructing extreme low-light images from raw sensor data. Despite their promising results, they still fail at recovering detailed textures and corresponding colors. To address these issues, we propose an Information Integration-and-Diffusion (InD) module to reconstruct excellent details from extreme low-light raw images. Precisely, a pixel-intensive global information matrix is calculated by separately integrating spatial-wise and channel-wise information and then diffusing them to each other by a matrix multiplication operation. In addition to this, we propose a Bottleneck Guided Channel Attention (BGCA) module to achieve unified channel information through low-light image enhancement networks for better color recovery. Extensive experimental results show that the networks equipped with our proposed modules outperform state-of-the-art approaches both quantitatively and visually.