Anchored neighborhood regression for fast example-based super-resolution

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 1920-1927

  Timofte, Radu ^a,b   De, Vincent ^a   Gool, Luc Van ^a,b  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b ETH ZURICH   (Switzerland)

Author keywords

anchored neighborhood regression; neighbor embedding; ridge regression; sparse coding; super resolution

Indexed keywords

REGRESSION ANALYSIS;

ANCHORED NEIGHBORHOOD REGRESSION; NEIGHBOR EMBEDDING; RIDGE REGRESSION; SPARSE CODING; SUPER RESOLUTION;

OPTICAL RESOLVING POWER;

EID: 84898792173     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.241     Document Type: Conference Paper

References (18)

1. M. Aharon, M. Elad, and A. Bruckstein. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. on Sig. Proc., 54(11), 2006.
2. S. Baker and T. Kanade. Hallucinating faces. In Automatic Face and Gesture Recognition, 2000.
3. M. Bevilacqua, A. Roumy, C. Guillemot, and M.-L. Alberi Morel. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In BMVC, 2012.
4. H. Chang, D.-Y. Yeung, and Y. Xiong. Super-resolution through neighbor embedding. CVPR, 01:275-282, 2004.
5. C. E. Duchon. Lanczos Filtering in One and Two Dimensions. J. Appl. Meteorology, 18:1016-1022, 1979.
6. S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar. Fast and robust multiframe super resolution. Trans. Img. Proc., 13(10):1327-1344, Oct. 2004.
7. R. Fransens, C. Strecha, and L. Van Gool. Optical flow based super-resolution: A probabilistic approach. Computer Vision and Image Understanding, 106(1):106-115, 2007.
8. W. T. Freeman, E. C. Pasztor, and O. T. Carmichael. Learning low-level vision. International Journal of Computer Vision, 40(1):25-47, 2000.
9. D. Glasner, S. Bagon, and M. Irani. Super-resolution from a single image. In ICCV, 2009.
10. X. Li and M. T. Orchard. New edge-directed interpolation. Trans. Img. Proc., 10(10):1521-1527, 2001.
11. M. Protter, M. Elad, H. Takeda, and P. Milanfar. Generalizing the non-local-means to super-resolution reconstruction. In Trans. Img. Proc., 2009.
12. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. In ICCV, 2001.
13. J. Sun, Z. Xu, and H.-Y. Shum. Image super-resolution using gradient profile prior. In CVPR, 2008.
14. A. N. Tikhonov and V. Y. Arsenin. Solution of Ill-posed Problems. Winston & Sons, 1977.
15. R. Timofte and L. Van Gool. Adaptive and weighted collaborative representations for image classification. Pattern Recognition Letters, 2013.
16. J. A. Tropp and A. C. Gilbert. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12), 2007.
17. J. Yang, J. Wright, T. Huang, and Y. Ma. Image superresolution via sparse representation. Image Processing, IEEE Transactions on, 19(11):2861-2873, 2010.
18. R. Zeyde, M. Elad, and M. Protter. On single image scale-up using sparse-representations. In Curves and Surfaces, pages 711-730, 2010.