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Weakly Constrained Lucy–Richardson with Applications to Inversion of Light Scattering Data

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Abstract

Lucy–Richardson (LR) is a classical iterative regularization method largely used for the restoration of nonnegative solutions. LR finds applications in many physical problems, such as for the inversion of light scattering data. In these problems, there is often additional information on the true solution that is usually ignored by many restoration methods because the related measurable quantities are likely to be affected by non-negligible noise. In this article we propose a novel Weakly Constrained Lucy–Richardson (WCLR) method which adds a weak constraint to the classical LR by introducing a penalization term, whose strength can be varied over a very large range. The WCLR method is simple and robust as the standard LR, but offers the great advantage of widely stretching the domain range over which the solution can be reliably recovered. Some selected numerical examples prove the performances of the proposed algorithm.

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Notes

  1. After the publication of [12], it was realized that the method called “modification of the Chahine algorithm” proposed in that work, is identical to the LR algorithm.

References

  1. Bai, Z.Z., Buccini, A., Hayami, K., Reichel, L., Ying, J.F., Zheng, N.: Modulus-based iterative methods for constrained Tikhonov regularization. J. Comput. Appl. Math. 319, 1–13 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  2. Baltes, H.P.: Inverse Scattering Problems in Optics. Springer, Berlin (1980)

    Book  Google Scholar 

  3. Bertero, M., Boccacci, P.: Introduction to Inverse Problems in Imaging. CRC Press, Boca Raton (1998)

    Book  MATH  Google Scholar 

  4. Biggs, D.S.C., Andrews, M.: Acceleration of iterative image restoration algorithms. Appl. Opt. 36(8), 1766–1775 (1997)

    Article  Google Scholar 

  5. Björck, Å.: Numerical Methods for Least Squares Problems. Siam, Philadelphia (1996)

    Book  MATH  Google Scholar 

  6. Bohren, C.F., Hirleman, E.D.: Feature on optical particle sizing. Appl. Opt. 30, 4685–4987 (1991)

    Article  Google Scholar 

  7. Brown, W.: Dynamic Light Scattering. Clarendon, Oxford (1993)

    Google Scholar 

  8. Calvetti, D., Landi, G., Reichel, L., Sgallari, F.: Non-negativity and iterative methods for ill-posed problems. Inverse Probl. 20(6), 1747 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  9. Engl, H.W., Hanke, M., Neubauer, A.: Regularization of Inverse Problems, vol. 375. Springer, Berlin (1996)

    Book  MATH  Google Scholar 

  10. Ferri, F., Bassini, A., Paganini, E.: Modified version of the Chahine algorithm to invert spectral extinction data for particle sizing. Appl. Optic. 34(25), 5829–5839 (1995)

    Article  Google Scholar 

  11. Ferri, F., Bassini, A., Paganini, E.: Commercial spectrophotometer for particle sizing. Appl. Optic. 36(4), 885–891 (1997)

    Article  Google Scholar 

  12. Ferri, F., Righini, G., Paganini, E.: Inversion of low-angle elastic light-scattering data with a new method devised by modification of the Chahine algorithm. Appl. Optic. 36(30), 7539–7550 (1997)

    Article  Google Scholar 

  13. Gazzola, S., Nagy, J.G.: Generalized Arnoldi–Tikhonov method for sparse reconstruction. SIAM J. Sci. Comput. 36(2), B225–B247 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Glasse, B., Riefler, N., Fritsching, U.: Intercomparison of numerical inversion algorithms for particle size determination of polystyrene suspensions using spectral turbidimetry. J. Spectrosc. 2015, 645–879 (2015)

    Google Scholar 

  15. Glatter, O.: Fourier transformation and deconvolution. In: Linder, P., Zemb, T. (eds.) Neutrons, X-rays and Light: Scattering Methods Applied to Soft Condensed Matter. Elsevier, North Holland (2002)

    Google Scholar 

  16. Glatter, O.: The inverse scattering problem in small angle scattering. In: Linder, P., Zemb, T. (eds.) Neutrons, X-rays and Light: Scattering Methods Applied to Soft Condensed Matter. Elsevier, North Holland (2002)

    Google Scholar 

  17. Glatter, O.: Static light scattering of large systems. In: Linder, P., Zemb, T. (eds.) Neutrons, X-rays and Light: Scattering Methods Applied to Soft Condensed Matter. Elsevier, North Holland (2002)

    Google Scholar 

  18. Gouesbet, G., Gréhan, G.: Optical Particle Sizing: Theory and Practice. Academic press, New York (1969)

    MATH  Google Scholar 

  19. Hanke, M., Hansen, P.C.: Regularization methods for large-scale problems. Surv. Math. Ind. 3(4), 253–315 (1993)

    MathSciNet  MATH  Google Scholar 

  20. Hilliard, J.E., Lawson, L.: Stereology and Stochastic Geometry, vol. 28. Kluwer Academic Publisher, The Netherlands (2003)

    MATH  Google Scholar 

  21. Hu, B., Shen, J., Duan, T.: Vector similarity measure for particle size analysis based on forward light scattering. Appl. Opt. 54, 3855–3862 (2015)

    Article  Google Scholar 

  22. Hulst, H.C.: Light Scattering by Small Particles. Dover Publications, New York (1981)

    Google Scholar 

  23. Kerker, M.: The scattering of light and other electromagnetic radiation. Academic Press, New York (1969)

    Google Scholar 

  24. Khan, M., Morigi, S., Reichel, L., Sgallari, F.: Iterative methods of Richardson–Lucy-type for image deblurring. Numer. Math. Theory Methods Appl 6(01), 262–275 (2013)

    MathSciNet  MATH  Google Scholar 

  25. Liu, X., Shen, J., Thomas, J.C., Clementi, L.A., Sun, X.: Multiangle dynamic light scattering analysis using a modified chahine method. J. Quant. Spectrosc. Radiat. Transf. 113, 489–497 (2012)

    Article  Google Scholar 

  26. Lucy, L.B.: An iterative technique for the rectification of observed distributions. Astron. J. 79, 745 (1974)

    Article  Google Scholar 

  27. Nagy, J.G., Strakoš, Z.: Enforcing nonnegativity in image reconstruction algorithms. In: International Symposium on Optical Science and Technology, pp. 182–190. International Society for Optics and Photonics (2000)

  28. Naiim, M., Boualem, A., Ferre, C., Jabloun, M., Jalocha, A., Ravier, P.: Multiangle multiangle dynamic light scattering for the improvement of multimodal particle size distribution measurements. Soft Matter 11, 28 (2015)

    Article  Google Scholar 

  29. Richardson, W.H.: Bayesian-based iterative method of image restoration. JOSA 62(1), 55–59 (1972)

    Article  Google Scholar 

  30. Roig, A.R., Alessandrini, J.L.: Particle size distributions from static light scattering with regularizednon-negative least squares constraints. Part. Part. Syst. Charact. 23, 431–437 (2006)

    Article  Google Scholar 

  31. Wang, L., Sun, X.G., Xing, J.: Determination of particle size distribution by light extinction method using improved pattern search algorithm with tikhonov smoothing functional. J. Mod. Opt. 59, 1829–1840 (2012)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the reviewers for their insightful comments that greatly improved the readability and the overall quality of this work. The work of the first two authors is supported in part by MIUR - PRIN 2012 N. 2012MTE38N and by a grant of the group GNCS of INdAM. The work of the last author was partially supported by Fondazione Cariplo, Grant n. 2016-0648, Project: Romancing the stone: size controlled HYdroxyaPATItes for sustainable Agriculture (HYPATIA).

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Correspondence to Marco Donatelli.

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Buccini, A., Donatelli, M. & Ferri, F. Weakly Constrained Lucy–Richardson with Applications to Inversion of Light Scattering Data. J Sci Comput 74, 786–804 (2018). https://doi.org/10.1007/s10915-017-0461-4

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  • DOI: https://doi.org/10.1007/s10915-017-0461-4

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