Fetching the paper…
Reading the bibliography…
This work presents TorchRadon -- an open source CUDA library which contains a set of differentiable routines for solving computed tomography (CT) reconstruction problems.
L. Landweber, An iteration formula for fredholm integral equations of the first kind , American Journal of Mathematics 73
1951
Earlier work this paper cites.
Jim Douglas, Alternating direction methods for three space variables , Numer. Math. 4
1962
Earlier work this paper cites.
Mark E. Davison, The ill-conditioned nature of the limited angle tomography problem , SIAM Journal on Applied Mathematics 43
1983
Earlier work this paper cites.
A. C. Kak and Malcolm Slaney, Principles of computerized tomographic imaging , IEEE Press, 1998
1998
Earlier work this paper cites.
Emmanuel J. Candes and David L. Donoho, Curvelets and reconstruction of images from noisy radon data , Wavelet Applications in Signal and Image Processing VIII (Akram Aldroubi, Andrew F. Laine, and Michael A. Unser, eds.), vol. 4119, International Society for Optics and Photonics, SPIE, 2000, pp. 108 – 117
2000
Earlier work this paper cites.
John D Hunter, Matplotlib: A 2d graphics environment , Computing in science & engineering 9
2007
Earlier work this paper cites.
Ignace Loris, Guust Nolet, Ingrid Daubechies, and F. A. Dahlen, Tomographic inversion using ℓ \ell 1-norm regularization of wavelet coefficients , Geophysical Journal International 170
2007
Earlier work this paper cites.
G. R. Easley, D. Labate, and F. Colonna, Shearlet-based total variation diffusion for denoising , IEEE Transactions on Image Processing 18
2009
Earlier work this paper cites.
2009
Cited alongside, same era.
Flavia Colonna, Glenn Easley, Kanghui Guo, and Demetrio Labate, Radon transform inversion using the shearlet representation , Applied and Computational Harmonic Analysis 29
2010
Cited alongside, same era.
Karol Gregor and Yann LeCun, Learning fast approximations of sparse coding , Proceedings of the 27th International Conference on International Conference on Machine Learning (Madison, WI, USA), ICML’10, Omnipress, 2010, p. 399–406
2010
Cited alongside, same era.
Stefan Loock and Gerlind Plonka, Phase retrieval for fresnel measurements using a shearlet sparsity constraint , Inverse Problems 30
2014
Cited alongside, same era.
M. T. McCann, K. H. Jin, and M. Unser, Convolutional neural networks for inverse problems in imaging: A review , IEEE Signal Processing Magazine 34
2017
Later among the works it cites.
Tim Meinhardt, Michael Moller, Caner Hazirbas, and Daniel Cremers, Learning proximal operators: Using denoising networks for regularizing inverse imaging problems , Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 1781–1790
2017
Later among the works it cites.
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer, Automatic differentiation in PyTorch , NIPS Autodiff Workshop, 2017
2017
Later among the works it cites.
Tatiana A. Bubba, Federica Porta, Gaetano Zanghirati, and Silvia Bonettini, A nonsmooth regularization approach based on shearlets for poisson noise removal in roi tomography , Applied Mathematics and Computation 318
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wim van Aarle, Willem Jan Palenstijn, Jan De Beenhouwer, Thomas Altantzis, Sara Bals, K. Joost Batenburg, and Jan Sijbers, The astra toolbox: A platform for advanced algorithm development in electron tomography , Ultramicroscopy 157
2015
Cited alongside, same era.
Wim van Aarle, Willem Jan Palenstijn, Jeroen Cant, Eline Janssens, Folkert Bleichrodt, Andrei Dabravolski, Jan De Beenhouwer, K. Joost Batenburg, and Jan Sijbers, Fast and flexible x-ray tomography using the astra toolbox , Opt. Express 24
2016
Cited alongside, same era.
T. A. Bubba, M. März, Z. Purisha, M. Lassas, and S. Siltanen, Shearlet-based regularization in sparse dynamic tomography , Wavelets and Sparsity XVII (Yue M. Lu, Dimitri Van De Ville, and Manos Papadakis, eds.), vol. 10394, International Society for Optics and Photonics, SPIE, 2017, pp. 236 – 245
2017
Cited alongside, same era.
Gitta Kutyniok, Volker Mehrmann, and Philipp C. Petersen, Regularization and numerical solution of the inverse scattering problem using shearlet frames , Journal of Inverse and Ill-posed Problems 25
2017
Cited alongside, same era.
M. Hanke, Conjugate gradient type methods for ill-posed problems. , New York: Chapman and Hall/CRC
Cited in the paper.
Simon Arridge, Peter Maass, Ozan Öktem, and Carola-Bibiane Schönlieb, Solving inverse problems using data-driven models , Acta Numerica 28
2019
Later among the works it cites.
Tatiana A Bubba, Gitta Kutyniok, Matti Lassas, Maximilian März, Wojciech Samek, Samuli Siltanen, and Vignesh Srinivasan, Learning the invisible: A hybrid deep learning-shearlet framework for limited angle computed tomography , 35
2019
Later among the works it cites.
Python Core Team, Python: A dynamic, open source programming language , Python Software Foundation, 2019
2019
Later among the works it cites.