2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-1.11
Paper Title INTEGRATION-AND-DIFFUSION NETWORK FOR LOW-LIGHT IMAGE ENHANCEMENT
Authors Pengliang Tang, Beijing University of Posts and Telecommunications, China; Xiaoqiang Guo, Academy of Broadcasting Science, China; Guodong Ju, Liangheng Shen, GuangDong TUS-TuWei Technology Co.,Ltd, China; Aidong Men, Beijing University of Posts and Telecommunications, China
SessionTEC-1: Restoration and Enhancement 1
LocationArea G
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.