Video denoising by online 3D sparsifying transform learning

Bihan Wen, Saiprasad Ravishankar, Yoram Bresler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Exploiting the sparsity of signals in an adaptive dictionary or transform domain benefits various applications in image/video processing. As opposed to synthesis dictionary learning, transform learning allows for cheap computations, and has been demonstrated to perform well in applications such as image denoising. Very recently, we proposed methods for online sparsifying transform learning, which are particularly useful for processing large-scale or streaming data. Online transform learning has good convergence guarantees and enjoys a much lower computational cost than online synthesis dictionary learning. In this work, we present a video denoising framework based on online 3D spatio-temporal sparsifying transform learning. The proposed scheme has low computational and memory costs, and can potentially handle streaming video. Our numerical experiments show promising performance for the proposed video denoising method compared to popular prior or state-of-the-art methods.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages118-122
Number of pages5
ISBN (Electronic)9781479983391
DOIs
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period9/27/159/30/15

Fingerprint

Glossaries
Image denoising
Video streaming
Processing
Costs
Data storage equipment
Experiments

Keywords

  • Big data
  • Denoising
  • Online learning
  • Sparse representations
  • Sparsifying transforms

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Wen, B., Ravishankar, S., & Bresler, Y. (2015). Video denoising by online 3D sparsifying transform learning. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings (pp. 118-122). [7350771] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December). IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7350771

Video denoising by online 3D sparsifying transform learning. / Wen, Bihan; Ravishankar, Saiprasad; Bresler, Yoram.

2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society, 2015. p. 118-122 7350771 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wen, B, Ravishankar, S & Bresler, Y 2015, Video denoising by online 3D sparsifying transform learning. in 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings., 7350771, Proceedings - International Conference on Image Processing, ICIP, vol. 2015-December, IEEE Computer Society, pp. 118-122, IEEE International Conference on Image Processing, ICIP 2015, Quebec City, Canada, 9/27/15. https://doi.org/10.1109/ICIP.2015.7350771
Wen B, Ravishankar S, Bresler Y. Video denoising by online 3D sparsifying transform learning. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society. 2015. p. 118-122. 7350771. (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2015.7350771
Wen, Bihan ; Ravishankar, Saiprasad ; Bresler, Yoram. / Video denoising by online 3D sparsifying transform learning. 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society, 2015. pp. 118-122 (Proceedings - International Conference on Image Processing, ICIP).
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