Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

R. J. Duffin and A. C. Schaeffer, “A class of nonharmonic Fourier series,” Transactions of the American Mathematical Society , vol. 72, no. 2, pp. 341–366, 1952

1952

Earlier work this paper cites.

R. E. Schapire, Y. Freund, P. Bartlett, W. S. Lee et al. , “Boosting the margin: A new explanation for the effectiveness of voting methods,” The annals of statistics , vol. 26, no. 5, pp. 1651–1686, 1998

1998

Earlier work this paper cites.

F. Noo, M. Defrise, and R. Clackdoyle, “Single-slice rebinning method for helical cone-beam CT,” Physics in medicine and biology , vol. 44, no. 2, p. 561, 1999

1999

Earlier work this paper cites.

M. Vetterli, P. Marziliano, and T. Blu, “Sampling signals with finite rate of innovation,” IEEE transactions on Signal Processing , vol. 50, no. 6, pp. 1417–1428, 2002

2002

Earlier work this paper cites.

A. Chambolle, “An algorithm for total variation minimization and applications,” Journal of Mathematical imaging and vision , vol. 20, no. 1, pp. 89–97, 2004

2004

Earlier work this paper cites.

J. Zhou, A. L. Cunha, and M. N. Do, “Nonsubsampled contourlet transform: construction and application in enhancement,” in IEEE International Conference on Image Processing 2005 , vol. 1. IEEE, 2005, pp. I–469

2005

Earlier work this paper cites.

T. Flohr, K. Stierstorfer, S. Ulzheimer, H. Bruder, A. Primak, and C. H. McCollough, “Image reconstruction and image quality evaluation for a 64-slice CT scanner with z-flying focal spot,” Medical physics , vol. 32, no. 8, pp. 2536–2547, 2005

2005

Earlier work this paper cites.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on image processing , vol. 16, no. 8, pp. 2080–2095, 2007

2007

Earlier work this paper cites.

E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Physics in medicine and biology , vol. 53, no. 17, p. 4777, 2008

2008

Earlier work this paper cites.

J.-F. Cai, R. H. Chan, and Z. Shen, “A framelet-based image inpainting algorithm,” Applied and Computational Harmonic Analysis , vol. 24, no. 2, pp. 131–149, 2008

2008

Earlier work this paper cites.

J.-F. Cai, R. H. Chan, L. Shen, and Z. Shen, “Convergence analysis of tight framelet approach for missing data recovery,” Advances in Computational Mathematics , vol. 31, no. 1-3, pp. 87–113, 2009

2009

Earlier work this paper cites.

E. J. Candès and B. Recht, “Exact matrix completion via convex optimization,” Foundations of Computational mathematics , vol. 9, no. 6, p. 717, 2009

2009

Earlier work this paper cites.

J.-F. Cai, E. J. Candès, and Z. Shen, “A singular value thresholding algorithm for matrix completion,” SIAM Journal on Optimization , vol. 20, no. 4, pp. 1956–1982, 2010

2010

Earlier work this paper cites.

H. H. Bauschke and P. L. Combettes, Convex analysis and monotone operator theory in Hilbert spaces . Springer, 2011

2011

Earlier work this paper cites.

M. Beister, D. Kolditz, and W. A. Kalender, “Iterative reconstruction methods in x-ray CT,” Physica medica , vol. 28, no. 2, pp. 94–108, 2012

2012

Earlier work this paper cites.

S. Ramani and J. A. Fessler, “A splitting-based iterative algorithm for accelerated statistical x-ray CT reconstruction,” IEEE transactions on medical imaging , vol. 31, no. 3, pp. 677–688, 2012

2012

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105

2012

Earlier work this paper cites.

H. C. Burger, C. J. Schuler, and S. Harmeling, “Image denoising: Can plain neural networks compete with BM3D?” in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2012, pp. 2392–2399

2012

Cited alongside, same era.

M. Li, Z. Fan, H. Ji, and Z. Shen, “Wavelet frame based algorithm for 3d reconstruction in electron microscopy,” SIAM Journal on Scientific Computing , vol. 36, no. 1, pp. B45–B69, 2014

2014

Cited alongside, same era.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 234–241

2015

Cited alongside, same era.

K. H. Jin and J. C. Ye, “Sparse+ low rank decomposition of annihilating filter-based Hankel matrix for impulse noise removal,” arXiv preprint arXiv:1510.05559 , 2015

Original

2015

Cited alongside, same era.

E. Kang, J. Min, and J. C. Ye, “A deep convolutional neural network using directional wavelets for low-dose x-ray ct reconstruction,” Medical Physics , vol. 44, no. 10, 2017

2017

Closest in time.

H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose ct with a residual encoder-decoder convolutional neural network,” IEEE transactions on medical imaging , vol. 36, no. 12, pp. 2524–2535, 2017

2017

Closest in time.

H. Chen, Y. Zhang, W. Zhang, P. Liao, K. Li, J. Zhou, and G. Wang, “Low-dose CT via convolutional neural network,” Biomedical optics express , vol. 8, no. 2, pp. 679–694, 2017

2017

Closest in time.

J. Adler and O. Öktem, “Learned primal-dual reconstruction,” arXiv preprint arXiv:1707.06474 , 2017

Original

2017

Closest in time.

H. Chen, Y. Zhang, W. Zhang, H. Sun, P. Liao, K. He, J. Zhou, and G. Wang, “Learned experts’ assessment-based reconstruction network (” learn”) for sparse-data ct,” arXiv preprint arXiv:1707.09636 , 2017