Resolution, linear filtering, and positivity

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Volumn 15, Issue 1, 1998, Pages 33-41

  Matson, Charles L ^a  

^a AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0003308560     PISSN: 10847529     EISSN: 15208532     Source Type: Journal    
DOI: 10.1364/JOSAA.15.000033     Document Type: Article

References (22)

1. G. Demoment, “Image reconstruction and restoration: overview of common estimation structures and problems,” IEEE Trans. Acoust. Speech Signal Process. 37, 2024–2036 (1989).
2. M. I. Sezan and A. M. Tekalp, “Survey of recent developments in digital image restoration,” Opt. Eng. 29, 393–404 (1990).
3. H. Stark, ed., Image Recovery: Theory and Application (Academic, Boston, Mass., 1987).
4. D. L. Fried, “Analysis of the CLEAN algorithm and implications for superresolution,” J. Opt. Soc. Am. A 12, 853–860 (1995).
5. N. Miura and N. Baba, “Superresolution for a nonnegative band-limited image,” Opt. Lett. 21, 1174–1176 (1996).
6. E. M. Haacke, Z. Liang, and S. H. Izen, “Superresolution reconstruction through object modeling and parameter estimation,” IEEE Trans. Acoust. Speech Signal Process. 37, 592–595 (1989).
7. Y. Vardi and D. Lee, “From image deblurring to optimal investments: maximum likelihood solutions for positive linear inverse problems,” J. Statist. Soc. B. 55, 569–612 (1993).
8. G. E. B. Archer and D. M. Titterington, “The iterative image space reconstruction algorithm (ISRA) as an alternative to the EM algorithm for solving positive linear inverse problems,” Stat. Sin. 5, 77–96 (1995).
9. A. E. Taylor and D. C. Lay, Introduction to Functional Analysis, 2nd ed. (Wiley, New York, 1980), p. 348.
10. C. L. Matson, “Fourier spectrum extrapolation and enhancement using support constraints,” IEEE Trans. Acoust. Speech, Signal Process. 42, 156–163 (1994).
11. C. L. Matson, “Variance reduction in Fourier spectra and their corresponding images with the use of support constraints,” J. Opt. Soc. Am. A 11, 97–106 (1994).
12. C. L. Matson, “Error reduction in images using high-quality prior knowledge,” Opt. Eng. 33, 3233–3236 (1994).
13. C. L. Matson, “The role of positivity for error reduction in images,” in Image and Signal Processing for Remote Sensing, J. Desachy, ed., Proc. SPIE 2315, 766–777 (1994).
14. A. Lannes, S. Roques, and M. J. Casanove, “Stabilized reconstruction in signal and image processing. I. Partial deconvolution and spectral extrapolation with limited field,” J. Mod. Opt. 34, 161–226 (1987).
15. A. Lannes, S. Roques, and M. J. Casanove, “Stabilized reconstruction in signal and image processing. II. Iterative reconstruction with and without constraint-interactive implementation,” J. Mod. Opt. 34, 321–370 (1987).
16. P. J. Sementilli, B. R. Hunt, and M. S. Nadar, “Analysis of the limit to superresolution in incoherent imaging,” J. Opt. Soc. Am. A 10, 2265–2276 (1993).
17. J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, San Francisco, Calif., 1968).
18. W. K. Pratt, Digital Image Processing, 2nd ed. (Wiley, New York, 1991), pp. 247–250 and 305.
19. T. Wilson and S. J. Hewlett, “Superresolution in confocal scanning microscopy,” Opt. Lett. 16, 1062–1064 (1991).
20. T. D. Milster and C. H. Curtis, “Analysis of superresolution in magneto-optic data storage devices,” Appl. Opt. 31, 6272–6279 (1992).
21. T. Suhara and H. Nishihara, “Theoretical analysis of superresolution readout of disc data by semiconfocal pickup heads,” Jpn. J. Appl. Phys. 31, 534–541 (1992).
22. F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proc. IEEE 66, 51–83 (1978)