Pre-print

  1. Y. Sun, J. Liu, M. Xie, B. Wohlberg, and U. S. Kamilov, “CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems.”
    [arXiv:2102.05181]
  2. J. Liu, Y. Sun, W. Gan, X. Xu, B. Wohlberg, and U. S. Kamilov, “SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees.”
    [arXiv:2101.09379]
  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, “Stochastic Deep Unfolding for Imaging Inverse Problems,” Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. (ICASSP 2021) (Toronto, Canada, June 6-11), in press.
  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), in press.
    [OpenReview] [arXiv:2010.01446] [Spotlight: 114/2997 = 4%]
  3. W. Gan, Y. Sun, C. Eldeniz, H. An and U. S. Kamilov, “Deep Image Reconstruction using Unregistered Measurements without Groundtruth,” Proc. Int. Symp. Biomedical Imaging 2021 (ISBI 2021) (Nice, France, April 13-16), in press.
    [arXiv:2009.13986]
  4. 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), in press.
    [arXiv:2002.11546]
  5. 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), in press.
  6. 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. 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]
  2. 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]
  3. M. Torop, S. Kothapalli, Y. Sun, J. Liu, S. Kahali, D. A. Yablonskiy, and U. S. Kamilov, “Deep learning using a biophysical model for Robust and Accelerated Reconstruction (RoAR) of quantitative and artifact-free R2* images,” Magn. Reson. Med., vol. 84, pp. 2932-2942, 2020.
    [doi:10.1002/mrm.28344] [arXiv:1912.07087]
  4. X. Xu, Y. Sun, J. Liu, B. Wohlberg, and U. S. Kamilov, “Provable Convergence of Plug-and-Play Priors with MMSE denoisers,” IEEE Signal Process. Lett., vol. 27, pp. 1280-1284, 2020.
    [doi:10.1109/lsp.2020.3006390] [arXiv:2005.07685]

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]