Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation
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Event data can be decompressed to retrieve a standard gray-scale visual representation, including via deep learning-based event-to-image methods.
“Recently, deep neural network-based event-to-image conversion models [8]-[11] have demonstrated impressive abilities in recovering gray-scale images from event streams, posing a potential risk to the privacy aspect of event-based vision applications.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [37]
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“This decompression has been achieved using different methods, such as patch-based dictionaries [36], variational models [37], or deep learning-based techniques [8], [9], [10], [11].”
Method
- Authors
- Munda, G., Reinbacher, C., & Pock, T.
- Affiliations
- Graz University of Technology (AT)
- Corresponding author
- Munda, Gottfried
- Venue
- International Journal of Computer Vision
- Published
- 2018-07-04
- Volume
- 126
- Issue
- 12
- Pages
- 1381-1393
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2964132169
- Cited by
- 181
- References
- 37
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 35 · 2023: 27 · 2022: 17 · 2021: 22 · 2020: 22 · 2019: 15 · 2018: 3
- FWCI
- 7.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- event cameras, variational model, real-time image reconstruction, asynchronous events, data term
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