Signal Processing - November 2017 - 95

He was the associate editor-in-chief of IEEE Transactions on
Medical Imaging from 2003 to 2005 and the founding chair of
the technical committee on Bioimaging and Signal Processing
of the IEEE Signal Processing Society (SPS). He is a member
of the editorial boards of SIAM Journal on Imaging Sciences
and Foundations and Trends in Signal Processing, a fellow of
EURASIP, a member of the Swiss Academy of Engineering
Sciences, and a Fellow of the IEEE. He received several international prizes, including three IEEE SPS Best Paper Awards
and two Technical Achievement Awards from the IEEE (2008
SPS and Engineering in Medicine and Biology 2010).

References

[1] J. Schmidhuber, "Deep learning in neural networks: An overview," Neural
Netw., vol. 61, pp. 85-117, Jan. 2015.
[2] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no.
7553, pp. 436-444, May 2015.
[3] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing:
Explorations in the Microstructure of Cognition, vol. 1. Cambridge, MA: MIT
Press, 1986, pp. 318-362.
[4] Y. T. Zhou, R. Chellappa, A. Vaid, and B. K. Jenkins, "Image restoration
using a neural network," IEEE Trans. Acoust., Speech, Signal Process., vol. 36,
no. 7, pp. 1141-1151, July 1988.
[5] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with
deep convolutional neural networks," in Proc. 25th Int. Conf. Neural Information
Processing Systems, Lake Tahoe, NV, 2012, pp. pp. 1097-1105.
[6] V. Jain and S. Seung, "Natural image denoising with convolutional networks,"
in Proc. 21st Int. Conf. Neural Information Processing Systems, Vancouver,
British Columbia, Canada, 2008, pp. 769-776.
[7] H. C. Burger, C. J. Schuler, and S. Harmeling, "Image denoising: Can plain
neural networks compete with BM3D?" in Proc. IEEE Conf. Computer Vision
and Pattern Recognition, Providence, RI, June 2012, pp. 2392-2399.
[8] J. Xie, L. Xu, and E. Chen, "Image denoising and inpainting with deep neural
networks," in Proc. 25th Int. Conf. Neural Information Processing Systems,
Lake Tahoe, NV, 2012, pp. 341-349.

[20] D. Li and Z. Wang, "Video super-resolution via motion compensation and
deep residual learning," IEEE Trans. Comput. Imaging, vol. PP, no. 99, pp. 1-1,
2017.
[21] S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang,
"Accelerating magnetic resonance imaging via deep learning," in Proc. IEEE 13th Int.
Symp. Biomedical Imaging, Apr. 2016, pp. 514-517.
[22] Y. Yang, J. Sun, H. Li, and Z. Xu, "Deep ADMM-net for compressive sensing
MRI," in Proc. 29th Int. Conf. Neural Information Processing Systems, Barcelona,
Spain, 2016, pp. 10-18.
[23] O. Oktay, W. Bai, M. Lee, R. Guerrero, K. Kamnitsas, J. Caballero, A. D.
Marvao, S. Cook, D. ORegan, and D. Rueckert, "Multi-input cardiac image superresolution using convolutional neural networks," in Proc. Medical Image
Computing and Computer-Assisted Intervention. Cham, Switzerland: Springer,
2016, pp. 246-254.
[24] D. M. Pelt and K. J. Batenburg, "Fast tomographic reconstruction from limited
data using artificial neural networks," IEEE Trans. Image Process, vol. 22, no. 12, pp.
5238-5251, Dec. 2013.
[25] D. Boublil, M. Elad, J. Shtok, and M. Zibulevsky, "Spatially-adaptive reconstruction in computed tomography using neural networks," IEEE Trans. Med. Imag., vol.
34, no. 7, pp. 1474-1485, July 2015.
[26] H. Chen, Y. Zhang, W. Zhang, P. Liao, K. Li, J. Zhou, and G. Wang, "Low-dose
CT via convolutional neural network," Biomed. Opt. Express, vol. 8, no. 2, pp. 679-
694, Feb. 2017.
[27] K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, "Deep convolutional neural
network for inverse problems in imaging," to be published.
[28] A. Kirsch, An Introduction to the Mathematical Theory of Inverse Problems,
New York: Springer, 2011, vol. 120.
[29] E. J. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: exact signal
reconstruction from highly incomplete frequency information," IEEE Trans. Inf.
Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
[30] C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A.
Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, "Photo-realistic single image superresolution using a generative adversarial network," arXiv Preprint, arXiv:1609.04802
[cs, stat], Sept. 2016.
[31] K. Gregor and Y. LeCun, "Learning fast approximations of sparse coding," in
Proc. 27th Int. Conf. Machine Learning, Haifa, Israel, 2010, pp. 399-406.
[32] K. Zhang, W. Zuo, S. Gu, and L. Zhang, "Learning deep CNN denoiser prior for
image restoration," arXiv Preprint, arXiv:1704.03264 [cs], Apr. 2017.
[33] T. M. Mitchell, Machine Learning. New York: McGraw-Hill Education, Mar.
1997.

[9] R. Wang and D. Tao, "Non-local auto-encoder with collaborative stabilization
for image restoration," IEEE Trans. Image Process., vol. 25, no. 5, pp. 2117-2129,
May 2016.

[34] H. Zhao, O. Gallo, I. Frosio, and J. Kautz, "Loss functions for image restoration
with neural networks," IEEE Trans. Comput. Imaging, vol. 3, no. 1, pp. 47-57, Mar.
2017.

[10] Y. Chen and T. Pock, "Trainable nonlinear reaction diffusion: a flexible
framework for fast and effective image restoration," IEEE Trans. Pattern Anal.
Mach. Intell, vol. 39, no. 6, pp. 1256 -1272, 2016.

[35] S. Bahrampour, N. Ramakrishnan, L. Schott, and M. Shah, "Comparative study
of deep learning software frameworks," arXiv Preprint, arXiv:1511.06435 [cs], Nov.
2015.

[11] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, "Beyond a Gaussian
denoiser: Residual learning of deep CNN for image denoising," IEEE Trans.
Image Process., vol. 26, no. 7, pp. 3142 -3155, July 2017.

[36] S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training
by reducing internal covariate shift," in Proc. Int. Conf. Machine Learning, 2015, Lille,
France, pp. 448-456.

[12] L. Xu, J. S. Ren, C. Liu, and J. Jia, "Deep convolutional neural network for
image deconvolution," in Proc. 27th Int. Conf. Neural Information Processing
Systems, Cambridge, MA, 2014, pp. 1790-1798.
[13] C. J. Schuler, M. Hirsch, S. Harmeling, and B. Schlkopf, "Learning to
deblur," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 7, pp. 1439-1451,
July 2016.
[14] K. Schawinski, C. Zhang, H. Zhang, L. Fowler, and G. K. Santhanam,
"Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit," Monthly Notices Royal Astronomical
Soc.: Lett., vol. 467, no. 1, pp. L110-L114, May 2017.
[15] Z. Cui, H. Chang, S. Shan, B. Zhong, and X. Chen, "Deep network cascade for
image super-resolution," in Proc. Computer Vision Conf., Sept. 2014, pp. 49-64.
[16] Z. Wang, D. Liu, J. Yang, W. Han, and T. Huang, "Deep networks for image
super-resolution with sparse prior," in Proc. IEEE Int. Conf. Computer Vision,
Santiago, Chile, 2015, pp. 370-378.
[17] J. Kim, J. Kwon Lee, and K. Mu Lee, "Accurate image super-resolution using
very deep convolutional networks," in Proc. IEEE Conf. Computer Vision and
Pattern Recognition, Las Vegas, NV, 2016, pp. 1646-1654.
[18] A. Kappeler, S. Yoo, Q. Dai, and A. K. Katsaggelos, "Video super-resolution
with convolutional neural networks," IEEE Trans. Comput. Imaging, vol. 2, no. 2,
pp. 109-122, June 2016.
[19] C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295-
307, Feb. 2016.

[37] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov,
"Dropout: A simple way to prevent neural networks from overfitting," J. Mach. Learn.
Res., vol. 15, no. 1, pp. 1929-1958, Jan. 2014.
[38] K. Hornik, "Approximation capabilities of multilayer feedforward networks,"
Neural Netw., vol. 4, no. 2, pp. 251-257, Mar. 1991.
[39] S. Mallat, "Understanding deep convolutional networks," Phil. Trans. R. Soc. A,
vol. 374, no. 2065, pp. 20150203, Apr. 2016.
[40] A. Choromanska, M. Henaff, M. Mathieu, G. Ben Arous, and Y. LeCun, "The
loss surfaces of multilayer networks," in Proc. 18th Int. Conf. Artificial Intelligence
and Statistics, 2015, pp. 192-204.
[41] J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, A. Senior, P.
Tucker, K. Yang, Q. V. Le, et al., "Large scale distributed deep networks," in Proc. 25th
Int. Conf. Neural Information Processing Systems, Lake Tahoe, Nevada, 2012, pp.
1223-1231.
[42] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A.
Courville, and Y. Bengio, "Generative adversarial nets," in Advances in Neural
Information Processing Systems, vol. 27, Z. Ghahramani, M. Welling, C. Cortes, N.
D. Lawrence, and K. Q. Weinberger, Eds. Cambridge, MA: Curran Associates, 2014,
pp. 2672-2680.
[43] J. Johnson, A. Alahi, and L. Fei-Fei, "Perceptual losses for real-time style transfer
and super-resolution," in Computer Vision ECCV 2016 (Lecture Notes Series in
Computer Science). Cham, Switzerland: Springer, 2016, pp. 694-711.

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Table of Contents for the Digital Edition of Signal Processing - November 2017

Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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