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D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13667))

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Abstract

Night imaging with modern smartphone cameras is troublesome due to low photon count and unavoidable noise in the imaging system. Directly adjusting exposure time and ISO ratings cannot obtain sharp and noise-free images at the same time in low-light conditions. Though many methods have been proposed to enhance noisy or blurry night images, their performances on real-world night photos are still unsatisfactory due to two main reasons: 1) Limited information in a single image and 2) Domain gap between synthetic training images and real-world photos (e.g., differences in blur area and resolution). To exploit the information from successive long- and short-exposure images, we propose a learning-based pipeline to fuse them. A D2HNet framework is developed to recover a high-quality image by deblurring and enhancing a long-exposure image under the guidance of a short-exposure image. To shrink the domain gap, we leverage a two-phase DeblurNet-EnhanceNet architecture, which performs accurate blur removal on a fixed low resolution so that it is able to handle large ranges of blur in different resolution inputs. In addition, we synthesize a D2-Dataset from HD videos and experiment on it. The results on the validation set and real photos demonstrate our methods achieve better visual quality and state-of-the-art quantitative scores. The D2HNet codes and D2-Dataset can be found at https://github.com/zhaoyuzhi/D2HNet.

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Notes

  1. 1.

    We thank Chao Wang in the SenseTime Research for helping capture the image.

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Authors and Affiliations

  1. Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China

    Yuzhi Zhao & Lai-Man Po

  2. SenseTime Research and Tetras.AI, Shenzhen, China

    Yongzhe Xu, Qiong Yan & Dingdong Yang

  3. MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China

    Xuehui Wang

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  1. Yuzhi Zhao
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  2. Yongzhe Xu
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  3. Qiong Yan
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  4. Dingdong Yang
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  5. Xuehui Wang
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  6. Lai-Man Po
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Correspondence to Yuzhi Zhao .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zhao, Y., Xu, Y., Yan, Q., Yang, D., Wang, X., Po, LM. (2022). D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_6

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