OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 41, Iss. 35 — Dec. 10, 2002
  • pp: 7464–7474

Image preprocessing for improving computational efficiency in implementation of restoration and superresolution algorithms

Malur K. Sundareshan, Supratik Bhattacharjee, Radhika Inampudi, and Ho-Yuen Pang  »View Author Affiliations


Applied Optics, Vol. 41, Issue 35, pp. 7464-7474 (2002)
http://dx.doi.org/10.1364/AO.41.007464


View Full Text Article

Enhanced HTML    Acrobat PDF (848 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Computational complexity is a major impediment to the real-time implementation of image restoration and superresolution algorithms in many applications. Although powerful restoration algorithms have been developed within the past few years utilizing sophisticated mathematical machinery (based on statistical optimization and convex set theory), these algorithms are typically iterative in nature and require a sufficient number of iterations to be executed to achieve the desired resolution improvement that may be needed to meaningfully perform postprocessing image exploitation tasks in practice. Additionally, recent technological breakthroughs have facilitated novel sensor designs (focal plane arrays, for instance) that make it possible to capture megapixel imagery data at video frame rates. A major challenge in the processing of these large-format images is to complete the execution of the image processing steps within the frame capture times and to keep up with the output rate of the sensor so that all data captured by the sensor can be efficiently utilized. Consequently, development of novel methods that facilitate real-time implementation of image restoration and superresolution algorithms is of significant practical interest and is the primary focus of this study. The key to designing computationally efficient processing schemes lies in strategically introducing appropriate preprocessing steps together with the superresolution iterations to tailor optimized overall processing sequences for imagery data of specific formats. For substantiating this assertion, three distinct methods for tailoring a preprocessing filter and integrating it with the superresolution processing steps are outlined. These methods consist of a region-of-interest extraction scheme, a background-detail separation procedure, and a scene-derived information extraction step for implementing a set-theoretic restoration of the image that is less demanding in computation compared with the superresolution iterations. A quantitative evaluation of the performance of these algorithms for restoring and superresolving various imagery data captured by diffraction-limited sensing operations are also presented.

© 2002 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.6640) Image processing : Superresolution

History
Original Manuscript: May 24, 2002
Revised Manuscript: August 20, 2002
Published: December 10, 2002

Citation
Malur K. Sundareshan, Supratik Bhattacharjee, Radhika Inampudi, and Ho-Yuen Pang, "Image preprocessing for improving computational efficiency in implementation of restoration and superresolution algorithms," Appl. Opt. 41, 7464-7474 (2002)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-41-35-7464


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. J. Goodman, Introduction to Fourier Optics (McGraw-Hill, New York, 1996).
  2. W. H. Richardson, “Bayesian-based iterative method of image restoration,” J. Opt. Soc. Am. 62, 55–60 (1972). [CrossRef]
  3. L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79, 745–759 (1974). [CrossRef]
  4. B. R. Hunt, “Super-resolution of images: Algorithms, principles and performance,” Int. J. Imaging Syst. Technol. 6, 297–304 (1995). [CrossRef]
  5. H. Y. Pang, M. K. Sundareshan, S. Amphay, “Optimized maximum-likelihood algorithms for superresolution of passive millimeter-wave imagery,” In Passive Millimeter-Wave Imaging Technology, R.M. Smith, ed., Proc. SPIE, 3378, 148–160 (1998)
  6. P. L. Combettes, “The foundations of set theoretic estimation,” Proc. of IEEE, 81, 182–208 (1993).
  7. M. I. Sezan, “An overview of convex projections theory and its application to image recovery problems,” Ultramicroscopy 40, 55–67 (1992) [CrossRef]
  8. S. Bhattacharjee, M. K. Sundareshan, “Hybrid Bayesian and convex set projections algorithms for restoration and resolution enhancement of digital images,” Proc. SPIEApplications of Digital Image Processing, E. Tischer, ed., 3922, 205–213 (2000).
  9. T. L. Ji, M. K. Sundareshan, H. Roehrig, “Adaptive image contrast enhancement based on human visual properties,” IEEE Trans. Med. Imaging 13, 573–586 (1994). [CrossRef] [PubMed]
  10. M. K. Sundareshan, P. Zegers, “Role of oversampled data in super-resolution processing and a progressive upsampling scheme for optimized implementations of iterative restoration algorithms”, in Passive Millimeter-Wave Imaging Technology, Aerosense’99, R. M. Smith, ed., 3703, 155–166 (1999).
  11. S. Bhattacharjee, “Super-resolution of images using a convex set-theoretic approach,” M.S. Thesis, (University of Arizona, Tucson, Arizona, 2000).
  12. D. H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 111–122 (1981).
  13. R. P. Lippman, “Pattern classification using neural networks,” IEEE Trans. Commun. 27, 47–64 (1989).
  14. R. Inampudi, “Region of interest extraction from images for optimized super-resolution performance,” M.S. thesis, (University of Arizona, Tucson, Arizona, 2000).
  15. B. R. Frieden, “Image enhancement and restoration,” in Picture Processing and Digital Filtering, T. S. Huang, ed. (Springer-Verlag, New York, 1975), pp. 177–247.
  16. B. R. Frieden, D. C. Wells, “Restoring with maximum entropy. III Poisson sources and backgrounds,” J. Opt. Soc. Am. 68, 93–103 (1978). [CrossRef]
  17. M. S. Nadar, P. J. Sementilli, B. R. Hunt, “Estimation techniques of the background and detailed portion of an object in image susperresolution,” in Inverse Optics III, M. A. Fiddy, ed., Proc. SPIE2241, 204–215 (1994).
  18. B. I. Hauss, H. Agravante, S. Chaiken, “Advanced radiometric millimeter-wave scene simulation: ARMSS,” in Passive Millimeter-Wave Imaging Technology, R. M. Smith, ed., Proc182–193 (1997).
  19. R. M. Haralick, L. G. Shapiro, Computer and Robot Vision (Addison-Wesley, New York, 1992).
  20. J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 372–381 (1986). [CrossRef]

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.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited