An edge-preserving super-precision for simultaneous enhancement of spacial and grayscale resolutions

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

Volumn E91-A, Issue 2, 2008, Pages 673-681

  Hasegawa, Hiroshi ^a   Ohtsuka, Toshinori ^b   Yamada, Isao ^b   Sakaniwa, Kohichi ^b  

^a NAGOYA UNIVERSITY   (Japan)

^b TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Huber function; Hybrid steepest descent method; Outer approximation; Set theoretic approach; Super resolution; Total variation

Indexed keywords

COST FUNCTIONS; DISCRETE COSINE TRANSFORMS; FUNCTIONS; IMAGE COMPRESSION; NUMERICAL METHODS; SET THEORY;

HUBER FUNCTION; HYBRID STEEPEST DESCENT METHOD; OUTER APPROXIMATION; SET-THEORETIC APPROACH; SUPER RESOLUTION; TOTAL VARIATION;

STEEPEST DESCENT METHOD;

EID: 77951173204     PISSN: 09168508     EISSN: 17451337     Source Type: Journal    
DOI: 10.1093/ietfec/e91-a.2.673     Document Type: Article

References (14)

1. S. C. Park, M. K. Park, and M. G. Kang, "Super-resolution image reconstruction-A technical overview, " IEEE Signal Process. Mag., vol. 20, no. 3, pp. 21-36, May 2003.
2. M. K. Ng and N. K. Bose, "Mathematical analysis of super-resolution methodology, " IEEE Signal Process. Mag., vol. 20, no. 3, pp. 62-74, May 2003.
3. B. Gunturk, Y. Altunbasak, and R. M. Mersereau, "Gray-scale resolution enhancement, " Proc. IEEE Workshop on Multimedia Signal Processing, pp. 155-160, 2001.
4. Y. Alutunbasak, A. J. Patti, and R. M. Mersereau, "Super-resolution still and video reconstruction from MPEG-coded video, " IEEE Trans. Circuits Syst. Video Technol., vol. 12, no. 4, pp. 217-226, April 2002.
5. C. A. Segall, R. Molina, and A. K. Katsaggelos, "High-resolution images from low-resolution compressed video, " IEEE Signal Process." Mag., vol. 20, no. 3, pp. 37-48, May 2003.
6. C. A. Segall, A. K. Katsaggelos, R. Molina, and J. Mateos, "Bayesian resolution enhancemnet of compressed video, " IEEE Trans. Image Process., vol. 13, no. 7, pp. 898-911, July 2004.
7. H. Stark and Y. Yang, Vector space projections - A numerical approach to signal and image processing, neural nets, and optics, John Wiley & Sons, 1998.
8. M. Elad and A. Feuer, "Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images, " IEEE Trans. Image Process., vol. 6, no. 12, pp. 1646-1658, Dec. 1997.
9. R. Schultz and R. Stevenson, "A Bayesian approach to image expansion for improved definition, " IEEE Trans. Image Process., vol. 3, no. 3, pp. 233-242, May 1994.
10. D. Capel and A. Zisserman, "Computer vision applied to super resolution, " IEEE Signal Process. Mag., vol. 20, no. 3, pp. 75-86, May 2003.
11. I. Yamada and N. Ogura, "Hybrid steepest descent method for variational inequality problem over the fixed point set of certain quasinonexpansive mappings, " Numerical Functional Analysis and Optimization, vol. 25, no. 7/8, pp. 619-655, 2004.
12. H. Hasegawa, T. Ono, I. Yamada, and K. Sakaniwa, "An iterative MPEG super-resolution with an outer approximation of framewise quantization constraint, " IEICE Trans. Fundamentals, vol. E88-A, no. 9, pp. 2427-2435, Sept. 2005.
13. H. Hasegawa, T. Ohtsuka, I. Yamada, and K. Sakaniwa, "An edgepreserving super-precision for simultaneous enhancement of spacial and grayscale resolutions, " Proc. 8th IEEE International Workshop on Multimedia Signal Processing, pp. 334-337, Dec. 2006.
14. D. Rajan, S. Chaudhuri, and M. V. Joshi, "Multi-objective super res-olution: Concept and examples, " IEEE Signal Process. Mag., vol. 20, no. 3, pp. 49-61, May 2003.