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Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network
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  • Published: 08 July 2022

Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network

  • Si-Qi Li  ORCID: orcid.org/0000-0001-9720-826X1,2,3,4,
  • Yue Gao  ORCID: orcid.org/0000-0002-4971-590X1,2,3,4 &
  • Qiong-Hai Dai1,2,3,5 

Machine Intelligence Research volume 19, pages 307–318 (2022)Cite this article

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Abstract

Seeing through dense occlusions and reconstructing scene images is an important but challenging task. Traditional frame-based image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions due to the lack of valid information available from the limited input occluded frames. Event cameras are bio-inspired vision sensors that record the brightness changes at each pixel asynchronously with high temporal resolution. However, synthesizing images solely from event streams is ill-posed since only the brightness changes are recorded in the event stream, and the initial brightness is unknown. In this paper, we propose an event-enhanced multi-modal fusion hybrid network for image de-occlusion, which uses event streams to provide complete scene information and frames to provide color and texture information. An event stream encoder based on the spiking neural network (SNN) is proposed to encode and denoise the event stream efficiently. A comparison loss is proposed to generate clearer results. Experimental results on a large-scale event-based and frame-based image de-occlusion dataset demonstrate that our proposed method achieves state-of-the-art performance.

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Acknowledgements

This work was supported by National Natural Science Funds of China (Nos. 62088102 and 62021002), and Beijing Natural Science Foundation, China (No. 4222025).

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Authors and Affiliations

  1. Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, 100084, China

    Si-Qi Li, Yue Gao & Qiong-Hai Dai

  2. Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, 100084, China

    Si-Qi Li, Yue Gao & Qiong-Hai Dai

  3. Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Tsinghua University, Beijing, 100084, China

    Si-Qi Li, Yue Gao & Qiong-Hai Dai

  4. Key Laboratory for Information System Security, School of Software, Tsinghua University, Beijing, 100084, China

    Si-Qi Li & Yue Gao

  5. Department of Automation, Tsinghua University, Beijing, 100084, China

    Qiong-Hai Dai

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  1. Si-Qi Li
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Corresponding author

Correspondence to Si-Qi Li.

Additional information

Colored figures are available in the online version at https://link.springer.com/journal/11633

Si-Qi Li received the B. Eng. degree in automation from School of Automation Science and Electrical Engineering, Beihang University, China in 2019. He is currently a Ph.D. degree candidate at School of Software, Tsinghua University, China.

His research interests include computer vision and machine learning.

Yue Gao received the B. Sc. degree in electronic and information engineering from Harbin Institute of Technology, China in 2005, and the M. Eng. degree in software engineering and the Ph. D. degree in control science and engineering from Tsinghua University, China in 2008 and 2012, respectively. He is an associate professor with School of Software, Tsinghua University, China.

His research interests include artificial intelligence and computer vision.

Qiong-Hai Dai received the M. Sc. and Ph. D. degrees in computer science and automation from Northeastern University, China in 1994 and 1996, respectively. He is currently a professor with Department of Automation and the Director of Institute for Brain and Cognitive Sciences, Tsinghua University, China. He has authored or coauthored over 200 conference articles and journal articles and two books.

His research interests include computational photography and microscopy, computer vision and graphics, and intelligent signal processing.

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Li, SQ., Gao, Y. & Dai, QH. Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network. Mach. Intell. Res. 19, 307–318 (2022). https://doi.org/10.1007/s11633-022-1350-3

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  • Received: 04 May 2022

  • Accepted: 14 June 2022

  • Published: 08 July 2022

  • Issue Date: August 2022

  • DOI: https://doi.org/10.1007/s11633-022-1350-3

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Keywords

  • Event camera
  • multi-modal fusion
  • image de-occlusion
  • spiking neural network (SNN)
  • image reconstruction
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