Skip to main content
Log in

Degradation reduction in optics imagery using Toeplitz structure

  • Published:
CALCOLO Aims and scope Submit manuscript

Abstract

This paper is concerned with improvement in optical image quality by image restoration. Image restoration is an ill-posed inverse problem which involves the removal or minimization of degradations caused by noise and blur in an image, resulting from, in this case, imaging through a medium. Our work here concerns the use of the underlying Toeplitz structure of such problems, and associated techniques for accelerating the convergence of iterative image restoration computations. Denoising methods, including total variation minimization, followed by segmentation-based preconditioning methods for minimum residual conjugate gradient iterations, are investigated. Regularization is accomplished by segmenting the image into (smooth) segments and varying the preconditioners across the segments. By taking advantage of the Toeplitz structure, our algorithms can be implemented with computational complexity of onlyO (ln 2 logn), wheren 2 is the number of pixels in the image andl is the number of segments used. Also, parallelization is straightforward. Numerical tests are reported for atmospheric imaging problems, including the case of spatially varying blur.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R. Acard andC. Vogel,Analysis of Bounded Variation Penalty Methods, Inverse Problems10, (1994) 1217–1229.

    Article  MathSciNet  Google Scholar 

  2. O. Axelsson, Iterative Solution Methods, Cambridge University Press, Cambridge, 1994.

    MATH  Google Scholar 

  3. A. Björck, Numerical Methods for Least Squares Problems, SIAM, Philadelphia, PA, 1996.

    MATH  Google Scholar 

  4. J. Brown,Overview of Topical Issues on Inverse Problems in Astronomy, Inverse Problems11, (1995) 635–638.

    Article  Google Scholar 

  5. K. Castleman, Digital Image Processing, Prentice-Hall, NJ, 1996.

    Google Scholar 

  6. P. J. Davis, Circulant Matrices, Wiley, New York, 1979.

    MATH  Google Scholar 

  7. D. Dobson andF. Santosa,Recovery of Blocky Images from Noisy and Blurred Data, SIAM. J. Appl. Math.56, (1996) 1181–1198.

    Article  MATH  MathSciNet  Google Scholar 

  8. G. Golub andC. Van Loan, Matrix Computations, The Johns Hopkins University Press, Baltimore, MD, 2nd Edition, 1989.

    MATH  Google Scholar 

  9. M. Hanke, Conjugate Gradient Type Methods for Ill-posed Problems, Pitman Research Notes in Mathematics, Longman Scientific & Technical, Harlow, Essex, 1995.

    MATH  Google Scholar 

  10. M. Hanke andP. Hansen,Regularization Methods for Large-scale Problems, Surveys Math. Indust.3, (1993) 253–315.

    MATH  MathSciNet  Google Scholar 

  11. M. Hanke andJ. Nagy,Restoration of Atmospherically Blurred Images by Symmetric Indefinite Conjugate Gradient Techniques, Inverse Problems12, (1996) 157–173.

    Article  MATH  MathSciNet  Google Scholar 

  12. M. Hanke, J. Nagy and R. Plemmons,Preconditioned Iterative Regularization for Ill-posed Problems, Numerical Linear Algebra and Scientific Computing, Eds. L. Reichel, A. Ruttan, R. Varga, Walter de Gruyter Press, Berlin 141–163.

  13. P. Hansen andD. P. O’Leary,The use of the L-Curve in the Regularization of Discrete Ill-posed Problems, SIAM J. Sci. Comp.14, (1993), 1487–1503.

    Article  MATH  MathSciNet  Google Scholar 

  14. J. W. Hardy,Adaptive-Optics, Scientific American270, no. 6 (1994) 60–65.

    Article  Google Scholar 

  15. R. Lagendijk andJ. Biedmond, Iterative Identification and Restoration of Images, Kluwer Press, Boston, 1991.

    MATH  Google Scholar 

  16. J. Nagy and D. P. O’Leary,Restoring Images Degraded by Space Variant Blur, Preprint, February 1995, to appear in SIAM J. Sci. Comput.

  17. J. Nagy, R. Plemmons andT. Torgersen,Iterative Image Restoration using Approximate Inverse Preconditioning, IEEE Trans. on Image Processing5, (1996) 1151–1162.

    Article  Google Scholar 

  18. S. Osher andL. Rudin,Feature-oriented Image Enhancement Using Shock Filters, SIAM J. Numer. Anal.27, (1990) 919–940.

    Article  MATH  Google Scholar 

  19. M. Paige andM. A. Saunders,Solution of Sparse Indefinite Systems of Linear Equations, SIAM J. Numer. Anal.12 (1975) 617–629.

    Article  MATH  MathSciNet  Google Scholar 

  20. S. Reaves,Optimal Space-Varying Regularization in Iterative Image Restoration, IEEE Trans. on Image Processing3, (1994) 319–324.

    Article  Google Scholar 

  21. L. Rudin andS. Osher,Total Variation Based Image Restoration with Free Local Constraints, Proc. IEEE International Conf. on Image Processing, II, (1994) 31–35.

    Article  Google Scholar 

  22. L. Rudin, S. Osher andE. Fatemi,Nonlinear Total Variation Based Noise Removal Algorithms, Physica D.60, (1992) 259–268.

    Article  MATH  Google Scholar 

  23. C. Vogel andM. Oman,Iterative Methods for Total Variation Denoising, SIAM J. Sci. Comput.17, (1996) 227–238.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

Research supported in part by a National Science Foundation Postdoctoral Research Fellowship.

Research sponsored by the U.S. Air Force Office of Scientific Research under grant F49620-97-1-1039.

Research sponsored by the U.S. Air Force Office of Scientific Research under grant F49620-97-1-0139, and by the National Science Foundation under grant CCR-96-23356.

Research sponsored by the National Science Foundation under grant CCR-96-23356.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nagy, J.G., Pauca, V.P., Plemmons, R.J. et al. Degradation reduction in optics imagery using Toeplitz structure. Calcolo 33, 269–288 (1996). https://doi.org/10.1007/BF02576005

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02576005

Keywords

Navigation