Pre-print

  1. Y. Sun, Z. Wu, B. Wohlberg, and U. S. Kamilov, “Scalable Plug-and-Play ADMM with Convergence Guarantees.”
    [arXiv:2006.03224]

In Press

  1. 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., in press.
    [doi:10.1002/mrm.28344] [arXiv:1912.07087]
  2. 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., in press.
    [doi:10.1109/jstsp.2020.2999820] [arXiv:1911.13241]
  3. 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., in press.
    [doi:10.1109/jstsp.2020.2998402] [arXiv:1912.05854] [supplement]

Recent Publications

  1. Y. Sun, J. Liu, and U. S. Kamilov, “Block Coordinate Regularization by Denoising,” IEEE Trans. Comput. Imag., vol. 6, pp. 908-921, 2020.
    [doi:10.1109/tci.2020.2996385] [arXiv:1905.05113]
  2. 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.
    [doi:10.1109/lsp.2020.3006390] [arXiv:2005.07685]
  3. G. Song, Y. Sun, J. Liu, Z. Wang, and U. S. Kamilov, “A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network,” IEEE Signal Process. Lett., vol. 27, pp. 451-455, 2020.
    [doi:10.1109/lsp.2020.2977214] [arXiv:1907.11793]
  4. Y. Sun, J. Liu, and U. S. Kamilov, “Block Coordinate Regularization by Denoising,” Proc. Ann. Conf. Neural Information Processing Systems (NeurIPS 2019) (Vancouver, Canada, December 8-14), pp. 382-392.
    [NeurIPS:8330] [arXiv:1905.05113]

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]