A Bayesian Approach to Image Expansion for Improved Definition

IEEE Transactions on Image Processing

Volumn 3, Issue 3, 1994, Pages 233-242

  Schultz, Richard R ^a   Stevenson, Robert L ^a  

^a UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ANALYSIS; MATHEMATICAL TECHNIQUES;

BAYESIAN TECHNIQUE; IMAGE EXPANSION; IMPROVED DEFINITION; MAXIMUM A POSTERIORI (MAP) ESTIMATION;

IMAGE PROCESSING;

EID: 0028427322     PISSN: 10577149     EISSN: 19410042     Source Type: Journal    
DOI: 10.1109/83.287017     Document Type: Article

References (32)

1. C. Bouman and K. Sauer, “A generalized Gaussian image model for edge-preserving MAP estimation,” IEEE Trans. Image Processing, vol. 2, no. 3, July 1993.
2. M. Brady and B. K. P. Horn, “Rotationally symmetric operators for surface interpolation,” Comput. Vision Graphics Image Processing, vol. 22, no. 1, pp. 70–94, 1983.
3. T. C. Chen and R. J. P. de Figueiredo, “Image decimation and interpolation techniques based on frequency domain analysis,” IEEE Trans. Commun., vol. COM-32, no. 4, pp. 479–484, 1984.
4. T. C. Chen and R. J. P. de Figueiredo, “Model-based image interpolation by generalized splines,” in Proc. Seventh Int. Conf. Patt. Recogn. (Montreal, Canada), July 30-Aug. 2, 1984, pp. 120–122.
5. T. C. Chen and R. J. P. de Figueiredo, “Two-dimensional interpolation by generalized spline filters based on partial differential equation image models,” IEEE Trans. Acoust. Speech Signal Processing, vol. ASSP-33, no. 3, pp. 631–642, 1985.
6. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-6, no. 6, pp. 721–741, 1984.
7. W. Grimson, From Images to Surfaces—A Computational Study of the Human Early Visual System. Cambridge, MA: MIT Press, 1981.
8. J. Hadamard, Lectures on the Cauchy Problem in Linear Partial Differential Equations. New Haven, CT: Yale University Press, 1923.
9. H. H. Hou and H. C. Andrews, “Cubic splines for image interpolation and digital filtering,” IEEE Trans. Acoust. Speech Signal Processing, vol. ASSP-26, no. 6, pp. 508–517, 1978.
10. P. J. Huber, Robust Statistics. New York: Wiley, 1981.
11. J. C. Hutchinson, J. Koch, J. Luo, and C. Mead, “Computing motion using analog and binary resistive networks,” Comput., vol. 21, no. 3, pp. 52–63, 1988.
12. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall, 1989.
13. N. B. Karayiannis and A. N. Venetsanopoulos, “Image interpolation based on variational principles,” Signal Processing, vol. 25, pp. 259–288, 1991.
14. R. G. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. Acoust. Speech Signal Processing, vol. ASSP-29, no. 6, pp. 1153–1160, 1981.
15. S. C. Kirkpatrick, C. D. Gelatt, Jr., and M. P. Vecchi, “Optimization by simulated annealing,” Sci., vol. 220, no. 4598, 671–680, 1983.
16. A. D. Kulkarni and K. Sivaraman, “Interpolation of digital imagery using hyperspace approximation,” Signal Processing, vol. 7, pp. 65–73, 1984.
17. S. Lakshmanan and H. Derin, “Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing,” IEEE Trans. Patt. Anal. Machine Intell., vol. 11, no. 8, pp. 799–813, 1989.
18. E. Levitan and G. T. Herman, “A maximum a posteriori probability expectation maximization algorithm for image reconstruction in emission tomography,” IEEE Trans. Med. Imaging, vol. MI-6, no. 3, pp. 185–192, 1987.
19. V. S. Nalwa, “Edge-detector resolution improvement by image interpolation,” IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-9, no. 3, pp. 446–451, 1987.
20. A. J. Parker, R. V. Kenyon, and D. E. Troxel, “Comparison of interpolating methods for image resampling,” IEEE Trans. Med. Imaging, vol. MI-2, no. 1, pp. 31–39, 1983.
21. T. Poggio, V. Torre, and C. Koch, “Computational vision and regularization theory,” Nature, vol. 317, no. 26, pp. 314–319, 1985.
22. W. F. Schreiber, Fundamentals of Electronic Imaging Systems (T. Huang, Ed.). New York: Springer-Verlag, 1986.
23. R. R. Schultz and R. L. Stevenson, “Improved definition image expansion,” in Proc. Int. Conf. Acoust. Speech Signal Processing, 1992, pp. III-173 to III-176.
24. R. R. Schultz and R. L. Stevenson, “Parameter estimation for discontinuity preserving stochastic signal models,” in Proc. Thirtieth Ann. Allerton Conf. Commun. Contr. Computing (Allerton, IL), Sept. 30-Oct. 3, 1992.
25. R. L. Stevenson, G. B. Adams III, L. H. Jamieson, and E. J. Delp, “Parallel implementation for iterative image restoration algorithms on a parallel DSP machine,” J. VLSI Signal Processing, vol. 5. pp. 261–272, 1993.
26. R. L. Stevenson, B. E. Schmitz and E. J. Delp, “Discontinuity preserving regularization of inverse visual problems,” IEEE Trans. Syst. Man Cybern., vol. 24, no. 3, Mar. 1994.
27. D. Terzopoulos, “Multilevel computational processes for visual surface reconstruction,” Comput. Vision Graphics Image Processing, vol. 24, 52–96, 1983.
28. A. M. Thompson, J. C. Brown, J. W. Kay, and D. M. Titterington, “A study of methods of choosing the smoothing parameter in image restoration by regularization,” IEEE Trans. Patt. Anal. Machine Intell., vol. 13, no. 4, pp. 326–339, 1991.
29. A. N. Tikhonov and V. Y. Arsenin, Solutions of III-Posed Problems. Washington, DC: V. H. Winston, 1977.
30. U. Unser, A. Aldroubi, and M. Eden, “Fast B-spline transforms for continuous image representation and interpolation,” IEEE Trans. Patt. Anal. Machine Intell., vol. 13, no. 3, pp. 277–285, 1991.
31. G. Wahba, “Spline bases, regularization and generalized cross validation for solving approximation problems with large quantities of noisy data,” Approximation Theory III (W. Cheney, Ed.). New York: Academic, 1980, pp. 905–912.
32. A. L. Yuille, “Energy functions for early vision and analog networks,” Biolog. Cybern., vol. 61, pp. 115–123, 1989.