research-article

Video super-resolution using motion compensation and classification-aided fusion

Authors:

Karen Simonyan,

Sergey Grishin,

Dmitriy Vatolin,

Dmitriy PopovAuthors Info & Claims

SCCG '08: Proceedings of the 24th Spring Conference on Computer Graphics

Pages 143 - 148

https://doi.org/10.1145/1921264.1921294

Published: 21 April 2008 Publication History

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Abstract

In this paper, we propose a super-resolution algorithm based on image fusion via pixel classification. Two high-resolution images are constructed, the first by means of motion compensation and the second by means of image interpolation. The AdaBoost classifier is then used in the fusion of these images, resulting in an high-resolution frame. Experimental results show that the proposed method outperforms well-known video resolution enhancement methods while maintaining moderate computational complexity.

References

[1]

Ahmad, I., Zheng, W. G., Luo, J. C., Liou, M. 2006. A Fast Adaptive Motion Estimation Algorithm. IEEE Transactions on Image Processing, vol. 16, issue 3, pp. 420--438.

Digital Library

Google Scholar

[2]

Bishop C., Blake A., and Marthi B. 2003. Super-Resolution Enhancement of Video. In C. M. Bishop and B. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics.

Google Scholar

[3]

Cheung V., Frey B. J., and Jojic N. 2005. Video Epitomes. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 42--49.

Digital Library

Google Scholar

[4]

Farsiu S., Elad M., and Milanfar P. 2006. A Practical Approach to Super-Resolution, Invited paper. In Proceedings of the SPIE Conference on Visual Communications and Image Processing, vol. 6077.

Google Scholar

[5]

Freeman W. T., Jones T. R., and Pasztor E. C. 2002. Example-Based Super-Resolution. In IEEE Computer Graphics and Applications, vol. 22(2), pp. 56--65.

Digital Library

Google Scholar

[6]

Friedman J. H., Hastie T., and Tibshirani R. 1998. Additive Logistic Regression: a Statistical View of Boosting. Dept. of Statistics, Stanford University Technical Report.

Google Scholar

[7]

Irani M., and Peleg S. 1991. Improving Resolution by Image Registration, Journal of Computer Vision, Graphics, and Image Processing, vol. 53(3), pp. 231--239.

Digital Library

Google Scholar

[8]

Kong D., Han M., Xu W., Tao H., and Gong Y. H. 2006. Video Super-Resolution with Scene-Specific Priors. In Proc. British Machine Vision Conference, pp. 549--558.

Google Scholar

[9]

Li X. 2006. Super-Resolution for Synthetic Zooming. EURASIP Journal on Applied Signal Processing, No. Article ID 58195, pp. 1--12.

Digital Library

Google Scholar

[10]

Wang Q., Tang X., and Shum H. 2005. Patch Based Blind Image Super Resolution. In Proceedings of IEEE Conference on Computer Vision, vol. 1, pp. 709--716.

Digital Library

Google Scholar

[11]

Jiang Z., Wong T. T., and Bao H. 2003. Practical Super-Resolution from Dynamic Video Sequences. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 549--554.

Google Scholar

Cited By

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Papanai ABabbar SPandey AKathuria HSharma AGupta N(2023)VIhanceD: Efficient Video Super Resolution for Edge Devices2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)10.1109/DELCON57910.2023.10127275(1-6)Online publication date: 24-Feb-2023
https://doi.org/10.1109/DELCON57910.2023.10127275

Index Terms

Video super-resolution using motion compensation and classification-aided fusion
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
  2. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Regression analysis

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Information

Published In

SCCG '08: Proceedings of the 24th Spring Conference on Computer Graphics

April 2008

196 pages

ISBN:9781605589572

DOI:10.1145/1921264

Conference Chair:
Karol Myszkowski
MPI Informatik, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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SCCG08

Sponsor:

Comenius University

SCCG08: Spring Conference on Computer Graphics

April 21 - 23, 2008

Budmerice, Slovakia

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Overall Acceptance Rate 67 of 115 submissions, 58%

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Papanai ABabbar SPandey AKathuria HSharma AGupta N(2023)VIhanceD: Efficient Video Super Resolution for Edge Devices2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)10.1109/DELCON57910.2023.10127275(1-6)Online publication date: 24-Feb-2023
https://doi.org/10.1109/DELCON57910.2023.10127275

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Video Super-Resolution Algorithm Using Bi-Directional Overlapped Block Motion Compensation and On-the-Fly Dictionary Training

Deep Residual Attention Network for Spectral Image Super-Resolution

Face image super-resolution through locality-induced support regression