Fourier-Domain Inversion for the Modulo Radon Transform (2307.13114v2)
Abstract: Inspired by the multiple-exposure fusion approach in computational photography, recently, several practitioners have explored the idea of high dynamic range (HDR) X-ray imaging and tomography. While establishing promising results, these approaches inherit the limitations of multiple-exposure fusion strategy. To overcome these disadvantages, the modulo Radon transform (MRT) has been proposed. The MRT is based on a co-design of hardware and algorithms. In the hardware step, Radon transform projections are folded using modulo non-linearities. Thereon, recovery is performed by algorithmically inverting the folding, thus enabling a single-shot, HDR approach to tomography. The first steps in this topic established rigorous mathematical treatment to the problem of reconstruction from folded projections. This paper takes a step forward by proposing a new, Fourier domain recovery algorithm that is backed by mathematical guarantees. The advantages include recovery at lower sampling rates while being agnostic to modulo threshold, lower computational complexity and empirical robustness to system noise. Beyond numerical simulations, we use prototype modulo ADC based hardware experiments to validate our claims. In particular, we report image recovery based on hardware measurements up to 10 times larger than the sensor's dynamic range while benefiting with lower quantization noise ($\sim$12 dB).
- M. Beckmann and A. Bhandari, “MR. TOMP: Inversion of the modulo Radon transform (MRT) via orthogonal matching pursuit (OMP),” in IEEE International Conference on Image Processing (ICIP), 2022, pp. 3748–3752.
- P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in ACM Transactions on Graphics. ACM Press, 1997.
- A. Bhandari, M. Beckmann, and F. Krahmer, “The modulo Radon transform and its inversion,” in European Signal Processing Conference (EUSIPCO), 2020, pp. 770–774.
- M. Beckmann, F. Krahmer, and A. Bhandari, “HDR tomography via modulo Radon transform,” in IEEE International Conference on Image Processing (ICIP), 2020, pp. 3025–3029.
- M. Beckmann, A. Bhandari, and F. Krahmer, “The modulo Radon transform: Theory, algorithms and applications,” SIAM Journal on Imaging Sciences, vol. 15, no. 2, pp. 455–490, 2022.
- Ž. Trpovski, V. Kerkez, and P. Vučinic, “HDR processing for legibility enhancement of radiographic images,” in Proc. of the HDRi 2013 First International Conference and SME Workshop on HDR imaging, 2013, pp. 31–36.
- P. Chen, Y. Han, and J. Pan, “High-dynamic-range CT reconstruction based on varying tube-voltage imaging,” PLOS ONE, vol. 10, no. 11, p. e0141789, Nov. 2015.
- M. A. Haidekker, L. D. Morrison, A. Sharma, and E. Burke, “Enhanced dynamic range x-ray imaging,” Computers in Biology and Medicine, vol. 82, pp. 40–48, Mar. 2017.
- J. T. Weiss, K. S. Shanks, H. T. Philipp, J. Becker, D. Chamberlain, P. Purohit, M. W. Tate, and S. M. Gruner, “High dynamic range x-ray detector pixel architectures utilizing charge removal,” IEEE Transactions on Nuclear Science, vol. 64, no. 4, pp. 1101–1107, Apr. 2017.
- Y. Li, Y. Han, and P. Chen, “X-ray energy self-adaption high dynamic range (HDR) imaging based on linear constraints with variable energy,” IEEE Photonics Journal, vol. 10, no. 2, pp. 1–14, Apr. 2018.
- P. Chen, S. Yang, Y. Han, J. Pan, and Y. Li, “High-dynamic-range x-ray CT imaging method based on energy self-adaptation between scanning angles,” OSA Continuum, vol. 3, no. 2, p. 253, Jan. 2020.
- A. Bhandari, F. Krahmer, and R. Raskar, “On unlimited sampling,” in Intl. Conf. on Sampling Theory and Applications (SampTA), Jul. 2017.
- ——, “On unlimited sampling and reconstruction,” IEEE Trans. Sig. Proc., vol. 69, pp. 3827–3839, Dec. 2020.
- A. Bhandari, F. Krahmer, and T. Poskitt, “Unlimited sampling from theory to practice: Fourier-Prony recovery and prototype ADC,” IEEE Trans. Sig. Proc., vol. 70, pp. 1131–1141, Sep. 2021.
- S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397–3415, 1993.
- M. Beckmann and A. Iske, “Error estimates and convergence rates for filtered back projection,” Mathematics of Computation, vol. 88, no. 316, pp. 801–835, 2019.
- ——, “Saturation rates of filtered back projection approximations,” Calcolo, vol. 57, no. 1, p. 12, 2020.
- M. Beckmann, P. Maass, and J. Nickel, “Error analysis for filtered back projection reconstructions in Besov spaces,” Inverse Problems, vol. 37, no. 1, p. 014002, 2021.
- H. Schomberg and J. Timmer, “The gridding method for image reconstruction by Fourier transformation,” IEEE Transactions on Medical Imaging, vol. 14, no. 3, pp. 569–607, 1995.
- D. Potts and G. Steidl, “A new linogram algorithm for computerized tomography,” IMA Journal of Numerical Analysis, vol. 21, no. 3, pp. 769–782, 2001.
- B. L. Sturm and M. G. Christensen, “Comparison of orthogonal matching pursuit implementations,” in European Signal Processing Conference (EUSIPCO), 2012, pp. 220–224.
- L. A. Shepp and B. F. Logan, “The Fourier reconstruction of a head section,” IEEE Transactions on Nuclear Science, vol. 21, no. 3, pp. 21–43, 1974.
- K. Hämäläinen, L. Harhanen, A. Kallonen, A. Kujanpää, E. Niemi, and S. Siltanen, “Tomographic X-ray data of a walnut,” 2015.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004.