Fast super-resolution via dense local training and inverse regressor search

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9005, Issue , 2015, Pages 346-359

  Pérez Pellitero, Eduardo ^a,b   Salvador, Jordi ^a   Torres Xirau, Iban ^a   Ruiz Hidalgo, Javier ^c   Rosenhahn, Bodo ^b  

^a Technicolor RandI Hannover   (Germany)

^b LEIBNIZ UNIVERSITY HANNOVER   (Germany)

^c UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

MAPPING; OPTICAL RESOLVING POWER;

HIGH RESOLUTION; LOCAL TRAINING; MAPPING FUNCTIONS; NONLINEAR MAPPINGS; OBJECTIVE QUALITIES; STATE OF THE ART; SUPER RESOLUTION; TRAINING SCHEMES;

INVERSE PROBLEMS;

EID: 84983609925     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-16811-1_23     Document Type: Conference Paper

References (21)

1. Tsai, R., Huang, T.: Multiple frame image restoration and registration. In: Proceedings of the Advances in Computer Vision and Image Processing, vol. 1, pp. 317–339 (1984)
2. Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP Graph. Models Image Process. 53, 231–239 (1991)
3. Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1167–1183 (2002)
4. Lin, Z., Shum, H.Y.: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans. Pattern Anal. Mach. Intell. 26, 83–97 (2004)
5. Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Trans. Comput. Graph. Appl. 22, 56–65 (2002)
6. Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: Proceedings of the IEEE International Conference on Computer Vision (2009)
7. Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 30, 12:1–12:11 (2011)
8. Yang, J., Lin, Z., Cohen, S.: Fast image super-resolution based on in-place example regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)
9. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861–2873 (2010)
10. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparserepresentations. In: Proceedings of the International Conference on Curves and Surfaces (2012)
11. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. (2004)
12. Timofte, R., Smet, V.D., Goool, L.V.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision (2013)
13. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54, 4311– 4322 (2006)
14. Peyré, G.: Manifold models for signals and images. Comput. Vis. Image Underst. 113, 249–260 (2009)
15. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
16. Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon, S.E.: Spherical hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)
17. Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, STOC 1998, pp. 604–613 (1998)
18. Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval (2010)
19. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing (2008)
20. He, K., Sun, J.: Computing nearest-neighbor fields via propagation-assisted kdtrees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)
21. Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, pp. 1–10 (2012)