TVR-DART: a more robust algorithm for discrete tomography from limited projection data with automated gray value estimation. In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.

References in zbMATH (referenced in 23 articles , 1 standard article )

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  1. Gondzio, Jacek; Lassas, Matti; Latva-Äijö, Salla-Maaria; Siltanen, Samuli; Zanetti, Filippo: Material-separating regularizer for multi-energy x-ray tomography (2022)
  2. Ceko, Matthew; Pagani, Silvia M. C.; Tijdeman, Rob: Algorithms for linear time reconstruction by discrete tomography. II (2021)
  3. Dong, Yiqiu; Wu, Chunlin; Yan, Shi: A fast method for simultaneous reconstruction and segmentation in X-ray CT application (2021)
  4. Kadu, Ajinkya; van Leeuwen, Tristan; Batenburg, K. Joost: CoShaRP: a convex program for single-shot tomographic shape sensing (2021)
  5. Holman, Sean; Richardson, Philip: SPECT with a multi-bang assumption on attenuation (2020)
  6. Jones, Glenn Arthur; Huthwaite, P.: Fast binary CT using Fourier null space regularization (FNSR) (2020)
  7. Lange, Jan-Hendrik; Pfetsch, Marc E.; Seib, Bianca M.; Tillmann, Andreas M.: Sparse recovery with integrality constraints (2020)
  8. Ruymbeek, Koen; Vanroose, Wim: Algorithm for the reconstruction of dynamic objects in CT-scanning using optical flow (2020)
  9. Dulio, Paolo; Pagani, Silvia M. C.: A rounding theorem for unique binary tomographic reconstruction (2019)
  10. Pagani, Silvia M. C.; Tijdeman, Rob: Algorithms for linear time reconstruction by discrete tomography (2019)
  11. Xu, Jinqiu; Zhao, Yunsong; Li, Hongwei; Zhang, Peng: An image reconstruction model regularized by edge-preserving diffusion and smoothing for limited-angle computed tomography (2019)
  12. Da, Yihui; Dong, Guirong; Wang, Bin; Liu, Dianzi; Qian, Zhenghua: A novel approach to surface defect detection (2018)
  13. Kojima, Takeshi; Ueta, Tetsushi; Yoshinaga, Tetsuya: Multivalued discrete tomography using dynamical system that describes competition (2017)
  14. Ringh, Axel; Zhuge, Xiaodong; Palenstijn, Willem Jan; Batenburg, Kees Joost; Öktem, Ozan: High-level algorithm prototyping: an example extending the TVR-DART algorithm (2017)
  15. Bleichrodt, Folkert; van Leeuwen, Tristan; Palenstijn, Willem Jan; van Aarle, Wim; Sijbers, Jan; Batenburg, K. Joost: Easy implementation of advanced tomography algorithms using the ASTRA toolbox with spot operators (2016)
  16. Zhuge, Xiaodong; Palenstijn, Willem Jan; Batenburg, Kees Joost: TVR-DART: a more robust algorithm for discrete tomography from limited projection data with automated gray value estimation (2016)
  17. Brunetti, Sara; Dulio, Paolo; Hajdu, Lajos; Peri, Carla: Ghosts in discrete tomography (2015)
  18. Cools, Siegfried; Ghysels, Pieter; van Aarle, Wim; Sijbers, Jan; Vanroose, Wim: A multi-level preconditioned Krylov method for the efficient solution of algebraic tomographic reconstruction problems (2015)
  19. Nemeth, Jozsef: Discrete tomography with unknown intensity levels using higher-order statistics (2015)
  20. Nagy, Ábris; Vincze, Csaba: Reconstruction of hv-convex sets by their coordinate X-ray functions (2014)

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