Single image super-resolution via sparse representation in gradient domain

Proceedings - 3rd International Conference on Multimedia Information Networking and Security, MINES 2011

Volumn , Issue , 2011, Pages 24-28

  Sun, Guangling ^a   Qin, Chuan ^b  

^a SHANGHAI UNIVERSITY   (China)

^b UNIVERSITY OF SHANGHAI FOR SCIENCE AND TECHNOLOGY   (China)

Author keywords

dictionary learning; gradient domain; Poisson equation; sparse representation; super resolution

Indexed keywords

DICTIONARY LEARNING; GENERIC IMAGES; GRADIENT DOMAIN; HIGH RESOLUTION IMAGE; IMAGE SUPER-RESOLUTION; INPUT IMAGE; LOW RESOLUTION IMAGES; RESEARCH TOPICS; SINGLE IMAGES; SPARSE REPRESENTATION; SUPER RESOLUTION; VISUAL PERCEPTION;

IMAGE PROCESSING; OPTICAL RESOLVING POWER; SECURITY OF DATA;

POISSON EQUATION;

EID: 84855678048     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MINES.2011.126     Document Type: Conference Paper

References (16)

1. Hardie, R.C., Barnard, K.J., Armstrong, E.A.: Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans. Image Process. 6(12), pp. 1621-1633 (1997)
2. Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super-resolution. IEEE Trans. Image Process. 13(10), pp. 1327-1344 (2004)
3. Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), pp. 1167-1183 (2002)
4. Sun, J., Xu, Z., Shum, H.: Image super-resolution using gradient profile prior. In Proc. IEEE Conf. Comput. Vision Pattern Recognit. (CVPR), pp. 1-8 (2008)
5. Hou, H.S., Andrews, H.C.: Cubic spline for image interpolation and digital filtering. IEEE Trans. Acoust. Speech Signal Process. 26(6), pp. 508-517 (1978)
6. Dai, S., Han, M. et al.: Soft edge smoothness prior for alpha channel super resolution. In Proc. IEEE Conf. Comput. Vision Pattern Recognit. (CVPR), pp. 1-8 (2007)
7. Freeman, W.T., Pazstor, E.C.: Learning low-level vision. Int. J. Comput. Vision 40(1), pp. 25-47 (2000)
8. Baker, S., Kanade, T.: Hallucinating faces. In Proc. IEEE Conf. Autom. Face Gest. Recogn., pp. 83-88 (2000)
9. Chang, H., Yeung, D., Xiong, Y.: Super-resolution through neighbor embedding. In Proc. IEEE Conf. Comput. Vision Pattern Recognit. (CVPR), vol. 1, pp. 275-282 (2004)
10. Yang, J., Wright J., et al.: Image super-resolution as sparse representation of raw image patches. In IEEE Conf. Comput. Vision Pattern Recognit. (CVPR), pp. 1-8 (2008)
11. Yang, J., Wright, J., Huang T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process., to be published (2010)
12. Michael Elad, Mário A.T. Figueiredo. On the role of sparse and redundant representations in image processing, Invited Paper, Proceedings of the IEEE - Special Issue on Applications of Sparse Representation and Compressive Sensing,(2009).
13. Aharon, M., Elad, M., Bruckstein, A.M.: The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans. Image Process. 54(11), pp. 4311-4322 (2006)
14. B. Efron, T. Hastie, I. Johnstone,R. Tibshirani. Least angle regression, Annals of Statistics, 32, pp.407-499 (2004)
15. S. J. Kasbah, I.W. Damaj, and R. A. Haraty, "Multigrid solvers in reconfigurable hardware," Journal of Computational and Applied Mathematics, 213(1), pp. 79-94, (2008).
16. Kodak Lossless True Color Image Suite, PhotoCD, http://r0k.us/graphics/ kodak/index.html