Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-27T18:06:24.706Z Has data issue: false hasContentIssue false

On the amount of detail that can be recovered from a degraded signal

Published online by Cambridge University Press:  01 July 2016

Peter Hall*
Affiliation:
University of Glasgow
*
This research was carried out while the author was on leave from the Department of Statistics, Faculty of Economics and Commerce, Australian National University, GPO Box 4, Canberra ACT 2601, Australia.

Abstract

Motivated by applications in digital image processing, we discuss information-theoretic bounds to the amount of detail that can be recovered from a defocused, noisy signal. Mathematical models are constructed for test-pattern, defocusing and noise. Using these models, upper bounds are derived for the amount of detail that can be recovered from the degraded signal, using any method of image restoration. The bounds are used to assess the performance of the class of linear restorative procedures. Certain members of the class are shown to be optimal, in the sense that they attain the bounds, while others are shown to be sub-optimal. The effect of smoothness of point-spread function on the amount of resolvable detail is discussed concisely.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1987 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

1. Andrews, H. C. and Hunt, B. R. (1977) Digital Image Restoration. Prentice-Hall, Englewood Cliffs, N. J. Google Scholar
2. Barnes, C. W. (1966) Object restoration in a diffraction-limited imaging system. J. Opt. Soc. Amer. 56, 575578.CrossRefGoogle Scholar
3. Bochner, S. and Martin, W. T. (1948) Several Complex Variables. Princeton University Press, Princeton, NJ. Google Scholar
4. Buck, E. J. and Gustincic, J. J. (1967) Resolution limitations of a finite aperture. IEEE Trans. Antennas and Propagation. AP-15, 376381.Google Scholar
5. Cathey, W. T., Frieden, B. R., Rhodes, W. T. and Rushforth, C. K. (1984) Image gathering and processing for enhanced resolution. J. Opt. Soc. Amer. Ser. A 1, 241250.Google Scholar
6. Chazan, D., Zakai, M. and Ziv, J. (1975) Improved lower bounds on signal parameter estimation. IEEE Trans. Inf. Theory. IT-21, 9093.CrossRefGoogle Scholar
7. Cunningham, D. R., Laramore, R. D. and Barrett, E. (1976) Detection in image-dependent noise. IEEE Inf. Theory IT-22, 603610.Google Scholar
8. Frieden, B. R. (1967) Band-unlimited reconstruction of optical objects and spectra. J. Opt. Soc. Amer. 57, 10131019.CrossRefGoogle Scholar
9. Frieden, B. R. (1970) Information and the restorability of images. J. Opt. Soc. Amer. 60, 575577.Google Scholar
10. Galbraith, R. F. and Galbraith, J. I. (1974) On the inverses of some patterned matrices arising in the theory of stationary time series. J. Appl. Prob. 11, 6371.Google Scholar
11. Gonsalves, R. A. (1976) Cramér-Rao bounds on mensuration errors. Appl. Optics. 15, 12701275.CrossRefGoogle ScholarPubMed
12. Grenader, U. (1981) Lectures in Pattern Theory , Vols. I-III. Springer-Verlag, New York.Google Scholar
13. Harris, J. L. (1964) Resolving power and decision theory. J. Opt. Soc. Amer. 54, 606611.Google Scholar
14. Harris, J. L. (1964) Diffraction and resolving power. J. Opt. Soc. Amer. 54, 931936.Google Scholar
15. Harris, J. L. (1966) Image evaluation and restoration. J. Opt. Soc. Amer. 56, 569574.Google Scholar
16. Helstrom, C. W. (1969) Detection and resolution of incoherent objects by a background-limited optical system. J. Opt. Soc. Amer. 59, 164175.Google Scholar
17. Helstrom, C. W. (1970) Resolvability of objects from the standpoint of statistical parameter estimation. J. Opt. Soc. Amer. 60, 659666.Google Scholar
18. Helstrom, C. W. (1977) Resolvable degrees of freedom in observation of a coherent object. J. Opt. Soc. Amer. 67, 833838.CrossRefGoogle Scholar
19. Huck, F. O., Fales, C. L., Jobson, D. J., Park, S. K. and Samms, R. W. (1984) Image-plane processing of visual information. Appl. Optics 23, 31603167.Google Scholar
20. Hunt, B. R. (1973) The application of constrained least squares estimation to image restoration by digital computer. IEEE Trans. Comput. C-22, 805812.Google Scholar
21. Mcglamery, B. L. (1967) Restoration of turbulence degraded images. J. Opt. Soc. Amer. 57, 295297.Google Scholar
22. Mueller, P. F. and Reynolds, G. O. (1967) Image restoration by removal of random-media degradations. J. Opt. Soc. Amer. 57, 13381344.CrossRefGoogle Scholar
23. Phillips, D. L. (1962) A technique for the numerical solution of certain integral equations of the first kind. J. Assoc. Comput. Mach. 9, 8489.Google Scholar
24. Riordan, J. (1968) Combinatorial Identities. Wiley, New York.Google Scholar
25. Rosenfeld, A. and Kak, A. C. (1982) Digital Picture Processing. Academic Press, New York.Google Scholar
26. Rushforth, C. K. and Harris, R. W. (1968) Restoration, resolution and noise. J. Opt. Soc. Amer. 58, 539545.Google Scholar
27. Shaman, P. (1973) On the inverse of the covariance matrix for an auto-regressive-moving average process. Biometrika 60, 193196.Google Scholar
28. Shaman, P. (1976) Approximations for stationary covariance matrices and their inverses with application to ARMA models. Ann. Statist. 4, 292301.Google Scholar
29. Titterington, D. M. (1985) General structure of regularization processes in image reconstruction. Astron. Astrophys. 144, 381387.Google Scholar
30. Twomey, S. (1965) The application of numerical filtering to the solution of integral equations encountered in indirect sensing measurements. J. Franklin Inst. 279, 95109.Google Scholar

Reference added in proof

31. Hall, P. (1987). On the processing of a motion-blurred image. SIAM J. Appl. Math. To appear.Google Scholar