Pre-print

  1. X. Xu, S. V. V. N. Kothapalli, J. Liu, S. Kahali, W. Gan, D. Yablonskiy, and U. S. Kamilov, “Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative R2* Mapping.”
    [arXiv:2109.01622]
  2. W. Gan, Y. Sun, C. Eldeniz, J. Liu, H. An, and U. S. Kamilov, “MoDIR: Motion-Compensated Training for Deep Image Reconstruction without Ground Truth.”
    [arXiv:2107.05533]
  3. Y. Sun, J. Liu, M. Xie, B. Wohlberg, and U. S. Kamilov, “CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems.”
    [arXiv:2102.05181]
  4. S. Kahali, S. V. V. N. Kothapalli, X. Xu, U. S. Kamilov, and D. Yablonskiy, “Deep-Learning-Based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient Recalled Echo (qGRE) MRI metrics associated with Human Brain Neuronal Structure and Hemodynamic Properties.”
    [doi:10.1101/2021.09.10.459810]

In Press

  1. J. Liu, S. Asif, B. Wohlberg, and U. S. Kamilov, “Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition,” Proc. Ann. Conf. Neural Information Processing Systems (NeurIPS 2021) (December 6-14), in press.
    [arXiv:2106.03668] [Acceptance rate: 2371/9122 = 26%]
  2. M. Xie, J. Liu, Y. Sun, B. Wohlberg, and U. S. Kamilov, “Joint Reconstruction and Calibration using Regularization by Denoising,” Proc. IEEE Int. Conf. Comp. Vis. Workshops (ICCVW 2021) (Oct 11-17), in press.
    [arXiv:2011.13391]
  3. W. Gan, Y. Hu, C. Eldeniz, J. Liu, Y. Chen, H. An, and U. S. Kamilov, “SS-JIRCS: Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI without Ground Truth,” Proc. IEEE Int. Conf. Comp. Vis. Workshops (ICCVW 2021) (Oct 11-17), in press.
  4. C. Eldeniz, 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]
  5. 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. Y. Sun, Z. Wu, X. Xu, B. Wohlberg, and U. S. Kamilov, “Scalable Plug-and-Play ADMM with Convergence Guarantees,” IEEE Trans. Comput. Imag., vol. 7, pp. 849-863, July 2021.
    [doi:10.1109/tci.2021.3094062] [arXiv:2006.03224]
  2. 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).
    [OpenReview] [arXiv:2010.01446] [Spotlight: 114/2997 = 4%]
  3. 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., vol. 7, pp. 598-610, June 2021.
    [doi:10.1109/tci.2021.3085534] [arXiv:2101.09379]
  4. 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]
  5. 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]

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. 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).
    [OpenReview] [arXiv:2010.01446] [Spotlight: 114/2997 = 4%]