“Fast image/video upsampling”
Conference:
Type(s):
Title:
- Fast image/video upsampling
Session/Category Title: Colourisation & upsampling
Presenter(s)/Author(s):
Abstract:
We propose a simple but effective upsampling method for automatically enhancing the image/video resolution, while preserving the essential structural information. The main advantage of our method lies in a feedback-control framework which faithfully recovers the high-resolution image information from the input data, without imposing additional local structure constraints learned from other examples. This makes our method independent of the quality and number of the selected examples, which are issues typical of learning-based algorithms, while producing high-quality results without observable unsightly artifacts. Another advantage is that our method naturally extends to video upsampling, where the temporal coherence is maintained automatically. Finally, our method runs very fast. We demonstrate the effectiveness of our algorithm by experimenting with different image/video data.
References:
1. Avidan, S., and Shamir, A. 2007. Seam carving for content-aware image resizing. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2007) 26, 10. Google Scholar
2. Baker, S., and Kanade, T. 2000. Limits on super-resolution and how to break them. In CVPR, IEEE Computer Society, 2372–2379.Google Scholar
3. Bando, Y., and Nishita, T. 2007. Towards digital refocusing from a single photograph. In Pacific Graphics. Google Scholar
4. Bhat, P., Zitnick, C. L., Snavely, N., Agarwala, A., Agrawala, M., Curless, B., Cohen, M., and Kang, S. B. 2007. Using photographs to enhance videos of a static scene. In Eurographics Symposium on Rendering, Eurographics, 327–338. Google Scholar
5. Bishop, C. M., Blake, A., and Marthi, B. 2003. Super-resolutioin enhancement of video. In In 9th Conf. on Artificial Intelligence and Statistics.Google Scholar
6. Chan, T. F., Osher, S., and Shen, J. 2001. The digital tv filter and nonlinear denoising. IEEE Trans. on Image Processing 10, 2, 231–241. Google ScholarDigital Library
7. Fattal, R. 2007. Image upsampling via imposed edges statistics. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2007) 26, 95. Google Scholar
8. Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., and Freeman, W. T. 2006. Removing camera shake from a single photograph. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2006) 25, 787–794. Google Scholar
9. Freeman, W. T., Jones, T. R., and Pasztor, E. C. 2002. Example-based super-resolution. IEEE Computer Graphics and Applications 22, 56–65. Google ScholarDigital Library
10. Frigo, M., and Johnson, S. G., 2006. FFTW Home Page. WWW page. http://www.fftw.org/.Google Scholar
11. Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., and Salesin, D. 2001. Image analogies. In SIGGRAPH, 327–340. Google Scholar
12. Huang, J. G., and Mumford, D. 1999. Statistics of natural images and models. In CVPR, 1541–1547.Google Scholar
13. Irani, M., and Peleg, S. 1993. Motion analysis for image enhancement: resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation 4, 324–335.Google ScholarCross Ref
14. Keys, R. G. 1981. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoustics, Speech, and Signal Processing 29, 1153–1160.Google ScholarCross Ref
15. Kong, D., Han, M., Xu, W., Tao, H., and Gong, Y. H. 2006. Video super-resolution with scene-specific priors. In BMVC.Google Scholar
16. Kopf, J., Cohen, M. F., Lischinski, D., and Uyttendaele, M. 2007. Joint bilateral upsampling. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2007) 26, 96. Google Scholar
17. Mon, D., 2006. Video enhancer. WWW page. http://www.thedeemon.com/VideoEnhancer/.Google Scholar
18. Osher, S., Sole, A., and Vese, L. 2003. Image decomposition and restoration using total variation minimization and the H
-1. Multiscale Modeling and Simulation 1, 3, 349–370.Google ScholarCross Ref
19. Patti, A., Sezan, M., and Tekalp, A. 1997. Super resolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Trans. on Image Processing 6, 1064–1076. Google ScholarDigital Library
20. QELabs, 2005. Qe super resolution. WWW page. http://www.qelabs.com/index.Google Scholar
21. Schultz, R. R., and Stevenson, R. L. 1996. Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing 5, 996–1011. Google ScholarDigital Library
22. Shan, Q., Jia, J. Y., and Agarwala, A. 2008. High-quality motion deblurring from a single image. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2008). Google Scholar
23. Sun, J., Sun, J., Xu, Z. B., and Shum, H. Y. 2008. Image super-rosolution using gradient profile prior. In CVPR.Google Scholar
24. Tappen, M. F., Russell, B. C., and Freeman, W. T. 2003. Exploiting the sparse derivative prior for super-resolution and image demosaicing. In Intl. Workshop on Statistical and Computational Theories of Vision.Google Scholar
25. Tappen, M. F., Russell, B. C., and Freeman, W. T. 2004. Efficient graphical models for processing images. In CVPR, IEEE Computer Society, 673–680. Google Scholar
26. Tipping, M. E., and Bishop, C. M. 2002. Bayesian image super resolution. In NIPS, MIT Press, 1279–1286.Google Scholar
27. Yuan, L., Sun, J., Quan, L., and Shum, H. Y. 2008. Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2008). Google Scholar
28. Zhao, W. Y., and Sawhney, H. S. 2002. Is super-resolution with optical flow feasible. In ECCV, Springer, 599–613. Google Scholar
