1. J. Liu, R. Anirudh, J. J. Thiagarajan, S. He, K. A. Mohan, U. S. Kamilov, and H. Kim, “DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction.”
  2. S. Shoushtari, J. Liu, and U. S. Kamilov, “DOLPH: Diffusion Models for Phase Retrieval.”
  3. J. Hu, S. Shoushtari, Z. Zou, J. Liu, Z. Sun, and U. S. Kamilov, “Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model.”
  4. W. Gan, H. Gao, Z. Sun, and U. S. Kamilov, “SINCO: A Novel structural regularizer for image compression using implicit neural representations.”
  5. X. Xu, W. Gan, S. V.V.N. Kothapalli, D. A. Yablonskiy, and U. S. Kamilov, “CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping.”
    [Project Page] [arXiv:2210.06330]
  6. W. Gan, C. Ying, P. Eshraghi, T. Wang, C. Eldeniz, Y. Hu, J. Liu, Y. Chen, H. An, and U. S. Kamilov, “Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees.”
  7. Y. Hu, W. Gan, C. Ying, T. Wang, C. Eldeniz, J. Liu, Y. Chen, H. An, and U. S. Kamilov, “SPICE: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation.”
    [Project Page] [arXiv:2210.02584]

In Press

  1. S. Shoushtari, J. Liu, Y. Hu, and U. S. Kamilov, “Deep Model-Based Architectures for Inverse Problems under Mismatched Priors,” IEEE J. Sel. Areas Inf. Theory, in press.
    [Project Page] [doi:10.1109/jsait.2022.3220044] [arXiv:2207.13200]
  2. J. Liu, X. Xu, W. Gan, S. Shoushtari, and U. S. Kamilov, “Online Deep Equilibrium Learning for Regularization by Denoising,” Proc. Ann. Conf. Neural Information Processing Systems (NeurIPS 2022) (New Orleans, LA, November 28-December 9), in press.
  3. U. S. Kamilov, C. A. Bouman, G. T. Buzzard, and B. Wohlberg, “Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging,” IEEE Signal Process. Mag., in press.
    [Project Page] [arXiv:2203.17061]
  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,” NMR Biomed., in press.
    [doi:10.1002/nbm.4883] [biorxiv]
  5. Z. Zhang, J. Liu, D. Yang, U. S. Kamilov, and G. Hugo, “Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction,” Med. Phys., in press.

Recent Publications

  1. J. Liu, R. Hyder, S. Asif, and U. S. Kamilov, “Optimization Algorithms for MR Reconstruction,” in Magnetic Resonance Image Reconstruction, M. Akcakaya, M. Doneva, C. Prieto, Eds. Elsevier, 2022, ch. 3, pp. 59–72.
  2. W. Shangguan, Y. Sun, W. Gan, and U. S. Kamilov, “Learning Cross-Video Neural Representations for High-Quality Frame Interpolation,” Proc. European Conference on Computer Vision (ECCV 2022) (Tel Aviv, Israel, October 23-27), pp. 511-528.
    [Project Page] [doi:10.1007/978-3-031-19784-0_30] [arXiv:2203.00137]
  3. Y. Hu, J. Liu, X. Xu, and U. S. Kamilov, “Monotonically Convergent Regularization by Denoising,” Proc. IEEE Int. Conf. Image Proc. (ICIP 2022) (Bordeaux, France, October 16-19), pp. 426-430.
    [doi:10.1109/icip46576.2022.9897639] [arXiv:2202.04961]
  4. A. H. Al-Shabili, X. Xu, I. Selesnick, and U. S. Kamilov, “Bregman Plug-and-Play Priors,” Proc. IEEE Int. Conf. Image Proc. (ICIP 2022) (Bordeaux, France, October 16-19), pp. 241-245.
    [10.1109/icip46576.2022.9897933] [arXiv:2202.02388]
  5. R. Liu, Y. Sun, J. Zhu, L. Tian, and U. S. Kamilov, “Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields,” Nat. Mach. Intell., vol. 4, pp. 781–791, September 2022.
    [Project Page] [doi:10.1038/s42256-022-00530-3] [arXiv:2112.00002]

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%]