Novel deblurring algorithms for images captured with CCD cameras

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

Volumn 14, Issue 7, 1997, Pages 1421-1430

  Wu, Chen Hsien ^a   Anderson, John M M ^a  

^a University of Florida   (United States)

Author keywords

Adaptive optics; Hermitian form optimization; Phasor reconstruction

Indexed keywords

EID: 0005377257     PISSN: 10847529     EISSN: 15208532     Source Type: Journal    
DOI: 10.1364/JOSAA.14.001421     Document Type: Article

References (19)

1. J. Janesick and T. Elliott, “History and advancements of large area array scientific CCD images,” in Astronomical CCD Observing and Reduction Techniques, Vol. 23 of ASP Conference Series, S. B. Howell, ed. (Astronomical Society of the Pacific, San Francisco, Calif., 1992), pp. 1–67.
2. R. L. Gilliland, “Details of noise sources and deduction pro-cesses,” in Astronomical CCD Observing and Reduction Techniques, Vol. 23 of ASP Conference Series, S. B. Howell, ed. (Astronomical Society of the Pacific, San Francisco, Calif., 1992), pp. 68–89.
3. D. L. Snyder, A. M. Hammoud, and R. L. White, “Image re-11. covery from data acquired with a chargecoupled-device camera,” J. Opt. Soc. Am. A 10, 1014–1023 (1993).
4. 12
5. 1, 113–122 (1982).
6. J. Llacer and J. Nunez, “Iterative maximum-likelihood and Bayesian algorithms for image reconstruction in astronomy,” in Restoration ofHubble Space Telescope Images
7. R. L. White and R. J. Allen, eds. (Space Telescope Science Institute, Baltimore, Md. 1990).
8. L. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79, 745–754 (1974).
9. W. H. Richardson, “Bayesian-based iterative method of image restoration,” J. Opt. Soc. Am. 62, 55–59 (1972).
10. D. L. Snyder, C. W. Helstrom, A. D. Lanterman, M. Faisal, 15 and R. L. White, “Compensation for readout noise in CCD images,” J. Opt. Soc. Am. A 12, 272–283 (1995).
11. J. Nunez and J. Llacer, “A general Bayesian image reconstruction algorithm with entropy prior preliminary application to HST data,” Astron. Soc. Pac. 105, 1192–1208 (1993).
12. N. L. Bonavito, J. E. Dorband, and T. Busse, “Maximum entropy restoration of blurred and oversaturated Hubble Space Telescope imagery,” Appl. Opt. 32, 5768–5774 (1993).
13. R. J. Hanisch and R. L. White, eds., Proceedings of Work-shop: The Restoration of HST Images and Spectra II (Space Telescope Science Institute, Baltimore, Md, 1993), pp. 62–69.
14. R. C. Puetter, “Pixon-based multiresolution image reconstruction and the quantification of picture information content,” Int. J. Imaging Syst. Technol. 6, 314–331 (1995).
15. R. L. White and R. J. Allen, eds., Proceedings ofWorkshop: The Restoration of HST Images and Spectra (Space Tele-scope Science Institute, Baltimore, Md., 1990).
16. R. L. White, “Restoration of images and spectra from the Hubble Space Telescope,” in Astronomical Data Analysis Software and System I, Vol. 25 of ASP Conference Series, D. M. Worrall, C. Biemesderfer, and J. Barnesetet, eds. (Astronomical Society of the Pacific, San Francisco, Calif., 1992), pp. 176–185.
17. J. M. M. Anderson, B. A. Mair, M. Rao, and C.-H. Wu, “Weighted least-squares algorithms for reconstructing positron emission tomography images,” IEEE Trans. Med. Im-aging 16, 159–165 (1997).
18. W. Feller, Introduction to Probability Theory and Its Applications (Wiley, New York, 1968), pp. 190, p. 245.
19. W. I. Zangwill, Nonlinear Programming (Prentice-Hall, New York, 1969), pp. 36–49.