Pre-print

  1. J. Liu, S. Asif, B. Wohlberg, and U. S. Kamilov, “Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition.”
    [arXiv:2106.03668]
  2. Y. Sun, J. Liu, M. Xie, B. Wohlberg, and U. S. Kamilov, “CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems.”
    [arXiv:2102.05181]
  3. Y. Sun, Z. Wu, B. Wohlberg, and U. S. Kamilov, “Scalable Plug-and-Play ADMM with Convergence Guarantees.”
    [arXiv:2006.03224]
  4. M. Xie, Y. Sun, J. Liu, B. Wohlberg, and U. S. Kamilov, “Joint Reconstruction and Calibration using Regularization by Denoising.”
    [arXiv:2011.13391]

In Press

  1. J. Liu, Y. Sun, W. Gan, X. Xu, B. Wohlberg, and U. S. Kamilov, “SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees,” IEEE Trans. Comput. Imag., in press.
    [doi:10.1109/tci.2021.3085534] [arXiv:2101.09379]
  2. E. Cihat, W. Gan, S. Chen, T. J. Fraum, D. R. Ludwig, Y. Yan, J. Liu, T. Vahle, U. B. Krishnamurthy, U. S. Kamilov, and H. An, “Phase2Phase: Respiratory Motion-Resolved Reconstruction of Free-Breathing Magnetic Resonance Imaging Using Deep Learning Without a Ground Truth for Improved Liver Imaging,” Invest. Radiol., in press.
    [doi:10.1097/rli.0000000000000792]
  3. Y. Sun, J. Liu, Y. Sun, B. Wohlberg, and U. S. Kamilov, “Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors,” Proc. Int. Conf. Learn. Represent. (ICLR 2021) (Vienna, Austria, May 4-8), in press.
    [OpenReview] [arXiv:2010.01446] [Spotlight: 114/2997 = 4%]
  4. W. Tahir, S. Gilbert, H. Wang, J. Zhu, U. S. Kamilov, and L. Tian, “Single-shot 3D holographic particle localization using deep priors trained on simulated data,” Proc. IS&T Electronic Imaging 2020 (Burlingame, CA, USA, January 26-30), in press.

Recent Publications

  1. W. Gan, C. Eldeniz, J. Liu, H. An, and U. S. Kamilov, “Image reconstruction for MRI using deep CNN priors trained without ground truth,” Proc. 54th Asilomar Conf. Signals, Systems, & Computers 2020, (Pacific Grove, CA, November 1–5), pp. 475-479.
    [doi:10.1109/ieeeconf51394.2020.9443403]
  2. X. Xu, J. Liu, Y. Sun, B. Wohlberg, and U. S. Kamilov, “Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling,” Proc. 54th Asilomar Conf. Signals, Systems, & Computers 2020, (Pacific Grove, CA, November 1–5), pp. 1305-1312.
    [doi:10.1109/ieeeconf51394.2020.9443410] [arXiv:2002.11546]
  3. 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, October 2020.
    [doi:10.1109/jstsp.2020.2999820] [arXiv:1911.13241]
  4. 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, October 2020.
    [doi:10.1109/jstsp.2020.2998402] [arXiv:1912.05854] [supplement]

Notable Publications

  1. U. S. Kamilov, V. K. Goyal, and S. Rangan, “Message-Passing De-Quantization with Applications to Compressed Sensing,” IEEE Trans. Signal Process., vol. 60, no. 12, pp. 6270–6281, December 2012.
    [doi:10.1109/tsp.2012.2217334] [arXiv:1105.6368] [IEEE SPS Best Paper Award 2017]
  2. U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, 
“Learning Approach to Optical Tomography,” Optica, vol. 2, no. 6, pp. 517–522, June 2015.
    [doi:10.1364/optica.2.000517] [Nature “News and Views”]
  3. H.-Y. Liu, U. S. Kamilov, D. Liu, H. Mansour, and P. T. Boufounos, “Compressive Imaging with Iterative Forward Models,” Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. (ICASSP 2017) (New Orleans, USA, March 5-9), pp. 6025-6029.
    [doi:10.1109/ICASSP.2017.7953313] [ICASSP 2017 Student Paper Award finalist]
  4. U. S. Kamilov, V. K. Goyal, and S. Rangan, “Generalized Approximate Message Passing Estimation from Quantized Samples,” Proc. 4th Int. Workshop on Computational Advances in Multi-Sensor Adaptive Process. (CAMSAP 2011) (San Juan, Puerto Rico, December 13-16), pp. 401-404.
    [10.1109/camsap.2011.6136027] [CAMSAP 2011 Student Paper Award finalist]
  5. U. S. Kamilov, S. Rangan, A. K. Fletcher, and M. Unser, “Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning,” Proc. Ann. Conf. Neural Information Processing Systems (NIPS 2012) (Lake Tahoe, Nevada, December 3-6), pp. 2447-2455.
    [nips:4498]