OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 20, Iss. 3 — Mar. 1, 2003
  • pp: 421–429

Experimental determination of object statistics from noisy images

Matthew A. Kupinski, Eric Clarkson, John W. Hoppin, Liying Chen, and Harrison H. Barrett  »View Author Affiliations


JOSA A, Vol. 20, Issue 3, pp. 421-429 (2003)
http://dx.doi.org/10.1364/JOSAA.20.000421


View Full Text Article

Enhanced HTML    Acrobat PDF (597 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Modern imaging systems rely on complicated hardware and sophisticated image-processing methods to produce images. Owing to this complexity in the imaging chain, there are numerous variables in both the hardware and the software that need to be determined. We advocate a task-based approach to measuring and optimizing image quality in which one analyzes the ability of an observer to perform a task. Ideally, a task-based measure of image quality would account for all sources of variation in the imaging system, including object variability. Often, researchers ignore object variability even though it is known to have a large effect on task performance. The more accurate the statistical description of the objects, the more believable the task-based results will be. We have developed methods to fit statistical models of objects, using only noisy image data and a well-characterized imaging system. The results of these techniques could eventually be used to optimize both the hardware and the software components of imaging systems.

© 2003 Optical Society of America

OCIS Codes
(030.4280) Coherence and statistical optics : Noise in imaging systems
(110.2960) Imaging systems : Image analysis
(110.3000) Imaging systems : Image quality assessment

History
Original Manuscript: June 10, 2002
Revised Manuscript: September 16, 2002
Manuscript Accepted: October 21, 2002
Published: March 1, 2003

Citation
Matthew A. Kupinski, Eric Clarkson, John W. Hoppin, Liying Chen, and Harrison H. Barrett, "Experimental determination of object statistics from noisy images," J. Opt. Soc. Am. A 20, 421-429 (2003)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-20-3-421


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. H. H. Barrett, “Objective assessment of image quality: effects of quantum noise and object variability,” J. Opt. Soc. Am. A 7, 1266–1278 (1990). [CrossRef] [PubMed]
  2. H. H. Barrett, J. L. Denny, R. F. Wagner, K. J. Myers, “Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance,” J. Opt. Soc. Am. A 12, 834–852 (1995). [CrossRef]
  3. H. H. Barrett, C. K. Abbey, E. Clarkson, “Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions,” J. Opt. Soc. Am. A 15, 1520–1535 (1998). [CrossRef]
  4. Z. Liu, D. C. Knill, D. Kersten, “Object classification for human and ideal observers,” Vision Res. 35, 549–568 (1995). [CrossRef] [PubMed]
  5. C. K. Abbey, H. H. Barrett, “Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,” J. Opt. Soc. Am. A 18, 473–488 (2001). [CrossRef]
  6. O. Schwartz, E. P. Simoncelli, “Natural signal statistics and sensory gain control,” Nat. Neuroscience 4, 819–825 (2001). [PubMed]
  7. H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part I (Academic, New York, 1968).
  8. S. C. Zhu, Y. Wu, D. Mumford, “Filters, random fields and maximum entropy (FRAME),” Int. J. Comput. Vision 27, 1–20 (1998). [CrossRef]
  9. E. P. Simoncelli, B. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1217 (2001). [CrossRef] [PubMed]
  10. H. H. Barrett, C. Abbey, B. Gallas, M. Eckstein, “Stabilized estimates of Hotelling-observer detection performance in patient-structured noise,” in Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE3340, 27–43 (1998). [CrossRef]
  11. J. P. Rolland, H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992). [CrossRef] [PubMed]
  12. F. Bochud, C. K. Abbey, M. Eckstein, “Statistical texture synthesis of mammographic images with clustered lumpy backgrounds,” Opt. Express 4, 33–43 (1999); http://www.opticsexpress.org . [CrossRef] [PubMed]
  13. H. H. Barrett, K. J. Myers, Foundations of Image Science (Wiley, New York, to be published).
  14. E. Clarkson, M. A. Kupinski, H. H. Barrett, “Transformation of characteristic functionals through imaging systems,” Opt. Express 10, 536–539 (2002); http://www.opticsexpress.org . [CrossRef] [PubMed]
  15. C. P. Robert, G. Casella, Monte Carlo Statistical Methods (Springer-Verlag, New York, 1999).
  16. W. R. Gilks, S. Richardson, D. J. Spiegelhalter, eds., Markov Chain Monte Carlo in Practice (Chapman & Hall, Boca Raton, Fla., 1996).
  17. K. J. Myers, H. H. Barrett, “Addition of a channel mechanism to the ideal-observer model,” J. Opt. Soc. Am. A 4, 2447–2457 (1987). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Figures

Fig. 1 Fig. 2 Fig. 3
 

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited