Home Modified Radon transform inversion using moments
Article
Licensed
Unlicensed Requires Authentication

Modified Radon transform inversion using moments

  • Hayoung Choi , Victor Ginting , Farhad Jafari EMAIL logo and Robert Mnatsakanov
Published/Copyright: April 9, 2019

Abstract

Moment methods to reconstruct images from their Radon transforms are both natural and useful. They can be used to suppress noise or other spurious effects and can lead to highly efficient reconstructions from relatively few projections. We establish a modified Radon transform (MRT) via convolution with a mollifier and obtain its inversion formula. The relationship of the moments of the Radon transform and the moments of its modified Radon transform is derived, and MRT data is used to provide a uniform approximation to the original density function. The reconstruction algorithm is implemented, and a simple density function is reconstructed from moments of its modified Radon transform. Numerical convergence of this reconstruction is shown to agree with the derived theoretical results.

References

[1] N. I. Akhiezer, The Classical Moment Problem and some Related Questions in Analysis, Hafner, New York, 1965. Search in Google Scholar

[2] E. J. Candés and D. L. Donoho, Curvelets and reconstruction of images from noisy Radon data, Wavelet Applications in Signal and Image Processing VIII, Proc. SPIE 4119, SPIE Press, Bellingham (2000), 108–117.10.1117/12.408569Search in Google Scholar

[3] S. Helgason, The Radon Transform, 2nd ed., Progr. Math. 5, Birkhäuser, Boston, 1999. 10.1007/978-1-4757-1463-0Search in Google Scholar

[4] K. Landmark, A. S. Solberg, F. Albregtsen, A. Austeng and R. E. Hansen, A radon-transform-based image noise filter with applications to multibeam bathymetry, IEEE Trans. Geosci. Remote Sens. 53 (2015), no. 11, 6252–6273. 10.1109/TGRS.2015.2436380Search in Google Scholar

[5] A. K. Louis, Corrigendum: “Approximate inverse for linear and some nonlinear problems” [Inverse Problems 11 (1995), no. 6, 1211–1223; MR1361769 (96f:65068)], Inverse Problems 12 (1996), no. 2, 175–190. 10.1088/0266-5611/12/2/005Search in Google Scholar

[6] A. K. Louis, A unified approach to regularization methods for linear ill-posed problems, Inverse Problems 15 (1999), no. 2, 489–498. 10.1088/0266-5611/15/2/009Search in Google Scholar

[7] A. K. Louis and P. Maass, A mollifier method for linear operator equations of the first kind, Inverse Problems 6 (1990), no. 3, 427–440. 10.1088/0266-5611/6/3/011Search in Google Scholar

[8] P. Milanfar, Geometric estimation and reconstruction from tomographic data, PhD dissertation, Massachusetts Institute of Technology, 1993. Search in Google Scholar

[9] P. Milanfar, W. Karl and A. Willsky, A moment-based variational approach to tomographic reconstruction, IEEE Trans. Image Process. 5 (1996), 459–470. 10.1109/83.491319Search in Google Scholar PubMed

[10] R. M. Mnatsakanov and S. Li, The Radon transform inversion using moments, Statist. Probab. Lett. 83 (2013), no. 3, 936–942. 10.1016/j.spl.2012.11.026Search in Google Scholar

[11] F. Natterer, The Mathematics of Computerized Tomography, Classics Appl. Math. 32, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, 2001. 10.1137/1.9780898719284Search in Google Scholar

[12] E. T. Quinto, An introduction to X-ray tomography and Radon transforms, The Radon Transform, Inverse Problems, and Tomography, Proc. Sympos. Appl. Math. 63, American Mathematical Society, Providence (2006), 1–23. 10.1090/psapm/063/2208234Search in Google Scholar

[13] E. T. Quinto, L. Ehrenpreis, A. Faridani, F. Gonzalez and E. Grinberg, Radon Transforms and Tomography, Contemp. Math. 278, American Mathematical Society, Providence, 2001. 10.1090/conm/278Search in Google Scholar

[14] K. T. Smith, D. C. Solmon and S. L. Wagner, Practical and mathematical aspects of the problem of reconstructing objects from radiographs, Bull. Amer. Math. Soc. 83 (1977), no. 6, 1227–1270. 10.1090/S0002-9904-1977-14406-6Search in Google Scholar

[15] J.-L. Starck, E. J. Candès and D. L. Donoho, The curvelet transform for image denoising, IEEE Trans. Image Process. 11 (2002), no. 6, 670–684. 10.1109/TIP.2002.1014998Search in Google Scholar PubMed

[16] E. M. Stein, Singular Integrals and Differentiability Properties of Functions, Princeton Math. Ser. 30, Princeton University, Princeton, 1970. 10.1515/9781400883882Search in Google Scholar

Received: 2018-09-26
Revised: 2019-03-05
Accepted: 2019-03-07
Published Online: 2019-04-09
Published in Print: 2020-02-01

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 30.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/jiip-2018-0090/html
Scroll to top button