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Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision.
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2018
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2018
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I. Y. Chun, X. Zheng, Y. Long, and J. A. Fessler, “BCD-Net for low- dose CT reconstruction: Acceleration, convergence, and generalization,” in Proc. Med. Image Computing and Computer Assist. Interven. , Shenzhen, China, Oct. 2019, pp. 31–40
2019
Closest in time.
2019
Closest in time.
H. K. Aggarwal, M. P. Mani, and M. Jacob, “MoDL: Model based deep learning architecture for inverse problems,” IEEE Trans. Med. Imag. , vol. 38, no. 2, pp. 394–405, Feb. 2019
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H. Lim, I. Y. Chun, J. A. Fessler, and Y. K. Dewaraja, “Improved low count quantitative SPECT reconstruction with a trained deep learning based regularizer,” J. Nuc. Med. (Abs. Book) , vol. 60, no. supp. 1, p. 42, May 2019
2019
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C. Crockett, D. Hong, I. Y. Chun, and J. A. Fessler, “Incorporating handcrafted filters in convolutional analysis operator learning for ill-posed inverse problems,” in Proc. IEEE Workshop CAMSAP , Guadeloupe, West Indies, Dec. 2019, pp. 316–320
2019
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E. T. Reehorst and P. Schniter, “Regularization by denoising: Clarifications and new interpretations,” IEEE Trans. Comput. Imag. , vol. 5, no. 1, pp. 52–67, Mar. 2019
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E. Ryu, J. Liu, S. Wang, X. Chen, Z. Wang, and W. Yin, “Plug-and-play methods provably converge with properly trained denoisers,” in Proc. ICML , Long Beach, CA, Jun. 2019, pp. 5546–5557
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D. Gilton, G. Ongie, and R. Willett, “Neumann networks for linear inverse problems in imaging,” IEEE Trans. Comput. Imag. , vol. 6, pp. 328–343, Oct. 2019
2019
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D. Zhang, Z. Xu, Z. Huang, A. R. Gutierrez, I. Y. Chun, C. J. Blocker, G. Cheng, Z. Liu, J. A. Fessler, Z. Zhong, and T. B. Norris, “Graphene-based transparent photodetector array for multiplane imaging,” in Proc. Conf. on Lasers and Electro-Optics , San Jose, CA, May 2019, p. SM4J.2
2019
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I. Y. Chun, Z. Huang, H. Lim, and J. A. Fessler, “Momentum-Net: Fast and convergent iterative neural network for inverse problems,” submitted , Jul. 2019
2019
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I. Y. Chun, X. Zheng, Y. Long, and J. A. Fessler, “BCD-Net for low- dose CT reconstruction: Acceleration, convergence, and generalization,” in Proc. Med. Image Computing and Computer Assist. Interven. , Shenzhen, China, Oct. 2019, pp. 31–40
2019
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S. Ye, Y. Long, and I. Y. Chun, “Momentum-net for low-dose CT image reconstruction,” arXiv preprint eess.IV:2002.12018 , Feb. 2020
2020
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I. Y. Chun and J. A. Fessler, “Convolutional analysis operator learning: Acceleration and convergence,” IEEE Trans. Image Process. , vol. 29, pp. 2108–2122, 2020
2020
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M.-B. Lien, C.-H. Liu, I. Y. Chun, S. Ravishankar, H. Nien, M. Zhou, J. A. Fessler, Z. Zhong, and T. B. Norris, “Ranging and light field imaging with transparent photodetectors,” Nature Photonics , vol. 14, no. 3, pp. 143–148, Jan. 2020
2020
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——, “Convolutional analysis operator learning: Acceleration and convergence,” IEEE Trans. Image Process. , vol. 29, pp. 2108–2122, 2020
2020
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M.-B. Lien, C.-H. Liu, I. Y. Chun, S. Ravishankar, H. Nien, M. Zhou, J. A. Fessler, Z. Zhong, and T. B. Norris, “Ranging and light field imaging with transparent photodetectors,” Nature Photonics , vol. 14, no. 3, pp. 143–148, Jan. 2020
2020
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