A convex approach for variational super-resolution

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

Volumn 6376 LNCS, Issue , 2010, Pages 313-322

  Unger, Markus ^a   Pock, Thomas ^a   Werlberger, Manuel ^a   Bischof, Horst ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

COMPREHENSIVE EVALUATION; CONVEX APPROACH; DESIGNED MODELS; DOWNSAMPLING; HIGH RESOLUTION IMAGE; IMAGE FORMATION PROCESS; LOW RESOLUTION VIDEO; MINIMIZATION PROBLEMS; MODEL COMPONENTS; PRIMAL DUAL ALGORITHMS; SUPER RESOLUTION; SYNTHETIC DATASETS; VARIATIONAL FRAMEWORK;

PATTERN RECOGNITION;

OPTICAL RESOLVING POWER;

EID: 78349257122     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15986-2_32     Document Type: Conference Paper

References (18)

1. Shannon, C.E.: A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev. 5(1) (2001) 3-55
2. Kosarev, E.L.: Shannon's superresolution limit for signal recovery. Inverse Problems 6(1) (1990) 55
3. Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002) 1167-1183
4. Protter, M., Elad, M., Takeda, H., Milanfar, P.: Generalizing the non-local-means to super-resolution reconstruction. In: IEEE Transactions on Image Processing. (2009) 36
5. Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: ICCV. (2009)
6. van Ouwerkerk, J.: Image super-resolution survey. Image and Vision Computing 24(10) (2006) 1039-1052
7. Mitzel, D., Pock, T., Schoenemann, T., Cremers, D.: Video super resolution using duality based TV-L1 optical flow. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 432-441. Springer, Heidelberg (2009)
8. Farsiu, S., Robinson, D., Elad, M., Milanfar, P.: Fast and robust multi-frame super-resolution. IEEE Transactions on Image Processing 13 (2003) 1327-1344
9. Huber, P.: Robust Statistics. Wiley, New York (1981)
10. Schoenemann, T.: Combinatorial Solutions for Shape Optimization in Computer Vision. PhD thesis, Department of Computer Science, University of Bonn, Germany (2008)
11. Shechtman, E., Caspi, Y., Irani, M.: Space-time super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005) 531-545
12. Takeda, H., Milanfar, P., Protter, M., Elad, M.: Super-resolution without explicit subpixel motion estimation. Trans. Img. Proc. 18(9) (2009) 1958-1975
13. Middlebury: http://vision.middlebury.edu/flow/ (2010) accessed 06-May-2010.
14. Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-L1 optical flow. In: BMVC, London, UK (September 2009)
15. Pock, T., Cremers, D., Bischof, H., Chambolle, A.: An algorithm for minimizing the mumford-shah functional. In: ICCV. (2009)
16. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D 60(1-4) (1992) 259-268
17. Esser, E., Zhang, X., Chan, T.F.: A general framework for a class of first order primal-dual algorithms for tv minimization. UCLA CAM Report [09-67] (August 2009)
18. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13 (2004) 600-612