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Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models.
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2011
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2011
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2013
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S. V. Venkatakrishnan, C. A. Bouman, and B. Wohlberg, “Plug-and-play priors for model based reconstruction,” in IEEE Global Conf. Signal Process. and Inf. Process. (GlobalSIP) , 2013, pp. 945–948
2013
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2014
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G. Alain and Y. Bengio, “What regularized auto-encoders learn from the data-generating distribution,” J. Mach. Learn. Res. , vol. 15, no. 1, p. 3563–3593, jan 2014
2014
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2015
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S. Sreehari, S. V. Venkatakrishnan, B. Wohlberg, G. T. Buzzard, L. F. Drummy, J. P. Simmons, and C. A. Bouman, “Plug-and-play priors for bright field electron tomography and sparse interpolation,” IEEE Trans. Comp. Imag. , vol. 2, no. 4, pp. 408–423, Dec 2016
2016
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A. M. Teodoro, J. M. Biocas-Dias, and M. A. T. Figueiredo, “Image restoration and reconstruction using variable splitting and class-adapted image priors,” in Proc. IEEE Int. Conf. Image Proc. (ICIP 2016) , September 25-28, 2016, pp. 3518–3522
2016
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U. S. Kamilov, H. Mansour, and B. Wohlberg, “A plug-and-play priors approach for solving nonlinear imaging inverse problems,” IEEE Signal Process. Lett. , vol. 24, no. 12, pp. 1872–1876, Dec 2017
2017
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2017
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T. Meinhardt, M. Moeller, C. Hazirbas, and D. Cremers, “Learning proximal operators: Using denoising networks for regularizing inverse imaging problems,” in Proc. IEEE Int. Conf. Comp. Vis. , Oct. 2017, pp. 1799–1808
2017
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2017
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S. H. Chan, X. Wang, and O. A. Elgendy, “Plug-and-play ADMM for image restoration: Fixed-point convergence and applications,” IEEE Trans. Comp. Imag. , vol. 3, no. 1, pp. 84–98, March 2017
2017
Cited alongside, same era.
Y. Romano, M. Elad, and P. Milanfar, “The little engine that could: Regularization by denoising (RED),” SIAM J. Imaging Sciences , vol. 10, no. 4, pp. 1804–1844, 2017
2017
Cited alongside, same era.
K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep CNN denoiser prior for image restoration,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR) , Jul. 2017, pp. 2808–2817
2017
Cited alongside, same era.
S. A. Bigdeli, M. Jin, P. Favaro, and M. Zwicker, “Deep mean-shift priors for image restoration,” in Proc. Advances in Neural Information Processing Systems 30 , Dec 4-9, 2017, pp. 763–772
2017
Cited alongside, same era.
J. Liu, Y. Sun, C. Eldeniz, W. Gan, H. An, and U. S. Kamilov, “RARE: Image reconstruction using deep priors learned without ground truth,” IEEE J. Sel. Topics Signal Process. , vol. 14, no. 6, pp. 1088–1099, 2020
2020
Later among the works it cites.
V. Sridhar, X. Wang, G. T. Buzzard, and C. Bouman, “Distributed iterative CT reconstruction using multi-agent consensus equilibrium,” IEEE Trans. Comp. Imag. , vol. 6, pp. 1153–1166, 2020
2020
Later among the works it cites.
X. Xu, Y. Sun, J. Liu, and U. S. Kamilov, “Provable convergence of plug-and-play priors with MMSE denoisers,” IEEE Signal Process. Lett. , vol. 27, pp. 1280–1284, 2020
2020
Later among the works it cites.
Z. Wu, Y. Sun, A. Matlock, J. Liu, L. Tian, and U. S. Kamilov, “SIMBA: Scalable inversion in optical tomography using deep denoising priors,” IEEE J. Sel. Topics Signal Process. , vol. 14, no. 6, pp. 1163–1175, 2020
2020
Later among the works it cites.
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2017
Cited alongside, same era.
G. T. Buzzard, S. H. Chan, S. Sreehari, and C. A. Bouman, “Plug-and-play unplugged: Optimization free reconstruction using consensus equilibrium,” SIAM J. Imaging Sci. , vol. 11, no. 3, pp. 2001–2020, 2018
2018
Cited alongside, same era.
J. Zhang and B. Ghanem, “ISTA-Net: Interpretable optimization inspired deep network for image compressive sensing,” in Proc. IEEE Conf. Comp. Vision and Pattern Recog. (CVPR) , 2018, pp. 1828–1837
2018
Cited alongside, same era.
X. Chen, J. Liu, Z. Wang, and W. Yin, “Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds,” in Proc. Advances in Neural Information Processing Systems 31 , 2018, pp. 9079–9089
2018
Cited alongside, same era.
A. Shocher, N. Cohen, and M. Irani, ““Zero-shot” super-resolution using deep internal learning,” in Proc. IEEE Conf. Comp. Vision and Pattern Recog. (CVPR) , June 2018
2018
Cited alongside, same era.
A. M. Teodoro, J. M. Bioucas-Dias, and M. Figueiredo, “A convergent image fusion algorithm using scene-adapted Gaussian-mixture-based denoising,” IEEE Trans. Image Process. , vol. 28, no. 1, pp. 451–463, Jan. 2019
2019
Cited alongside, same era.
E. T. Reehorst and P. Schniter, “Regularization by denoising: Clarifications and new interpretations,” IEEE Trans. Comp. Imag. , vol. 5, no. 1, pp. 52–67, 2019
2019
Cited alongside, same era.
Y. Sun, B. Wohlberg, and U. S. Kamilov, “An online plug-and-play algorithm for regularized image reconstruction,” IEEE Trans. Comp. Imag. , vol. 5, no. 3, pp. 395–408, Sep. 2019
2019
Cited alongside, same era.
——, “Back-projection based fidelity term for ill-posed linear inverse problems,” IEEE Trans. Image Process. , vol. 29, pp. 6164–6179, 2020
2020
Later among the works it cites.
A. Gnanasambandam and S. Chan, “One size fits all: Can we train one denoiser for all noise levels?” in Proc. 37th Int. Conf. Machine Learning (ICML) , vol. 119, 13–18 Jul 2020, pp. 3576–3586
2020
Later among the works it cites.
S. A. Hussein, T. Tirer, and R. Giryes, “Correction filter for single image super-resolution: Robustifying off-the-shelf deep super-resolvers,” in Proc. IEEE Conf. Comp. Vision and Pattern Recog. (CVPR) , 2020, pp. 1425–1434
2020
Later among the works it cites.
Y. Sun, Z. Wu, X. Xu, B. Wohlberg, and U. S. Kamilov, “Scalable plug-and-play ADMM with convergence guarantees,” IEEE Trans. Comp. Imag. , vol. 7, pp. 849–863, Jul. 2021
2021
Later among the works it cites.
J. Liu, S. Asif, B. Wohlberg, and U. S. Kamilov, “Recovery analysis for plug-and-play priors using the restricted eigenvalue condition,” in Proc. Advances in Neural Information Processing Systems 35 , December 6-14, 2021
2021
Later among the works it cites.
V. Monga, Y. Li, and Y. C. Eldar, “Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,” IEEE Signal Process. Mag. , vol. 38, no. 2, pp. 18–44, Mar. 2021
2021
Later among the works it cites.
D. Gilton, G. Ongie, and R. Willett, “Deep equilibrium architectures for inverse problems in imaging,” IEEE Trans. Comp. Imag. , vol. 7, pp. 1123–1133, Oct. 2021
2021
Later among the works it cites.
S. Majee, T. Balke, C. A. J. Kemp, G. T. Buzzard, and C. A. Bouman, “Multi-slice fusion for sparse-view and limited-angle 4D CT reconstruction,” IEEE Trans. Comp. Imag. , vol. 7, pp. 448–462, 2021
2021
Later among the works it cites.
C. A. Bouman, Foundations of Computational Imaging: A Model-Based Approach . Philadelphia: Society for Industrial and Applied Mathematics, 2022
2022
Closest in time.
W. Gan, Y. Sun, C. Eldeniz, J. Liu, H. An, and U. S. Kamilov, “Deformation-compensated learning for image reconstruction without ground truth,” IEEE Trans. Med. Imag. , 2022, doi:10.1109/TMI.2022.3163018
2022
Closest in time.
T. Balke, F. Davis, C. Garcia-Cardona, M. McCann, L. Pfister, and B. Wohlberg, “Scientific Computational Imaging COde (SCICO),” Software library available from https://github.com/lanl/scico , 2022
2022
Closest in time.
E. J. Reid, L. F. Drummy, C. A. Bouman, and G. T. Buzzard, “Multi-resolution data fusion for super resolution imaging,” IEEE Trans. Comp. Imag. , vol. 8, pp. 81–95, 2022
2022
Closest in time.