Publications

Pre-print

  1. Y. Hu, A. Peng, W. Gan, P. Milanfar, M. Delbracio, and U. S. Kamilov, “Stochastic Deep Restoration Priors for Imaging Inverse Problems.”
    [Project Page] [arXiv:2410.02057]
  2. C. Y. Park, M. T. McCann, C. Garcia-Cardona, B. Wohlberg, and U. S. Kamilov, “Random Walks with Tweedie: A Unified Framework for Diffusion Models.”
    [Project Page] [arXiv:2411.18702] [code]
  3. Y. Hu, A. Peng, W. Gan, and U. S. Kamilov, “ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction.”
    [Project Page] [arXiv:2411.16535]
  4. M. Terris, U. S. Kamilov, and T. Moreau, “FiRe: Fixed-points of Restoration Priors for Solving Inverse Problems.”
    [arXiv:2411.18970]
  5. N. Yismaw, U. S. Kamilov, and M. S. Asif, “Gaussian is All You Need: A Unified Framework for Solving Inverse Problems via Diffusion Posterior Sampling.”
    [arXiv:2409.08906]
  6. S. Shoushtari, E. P. Chandler, J. Zhang, M. Senanayake, S. V. Pingali, M. Foston, and U. S. Kamilov, “PnP Restoration with Domain Adaptation for SANS.”
    [arXiv:2403.10495]
  7. 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]
  8. C. Park, W. Gan, Z. Zou, Y. Hu, Z. Sun, U. S. Kamilov, “A Structured Pruning Algorithm for Model-based Deep Learning.”
    [arXiv:2311.02003]
  9. H. Xie, W. Gan, B. Zhou, X. Chen, Q. Liu, X. Guo, L. Guo, H. An, U. S. Kamilov, G. Wang, C. Liu, “DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging.”
    [arXiv:2311.04248]
  10. W. Gan, H. Xie, C. von Gall, G. Platsch, M. T. Jurkiewicz, A. Andrade, U. C. Anazodo, U. S. Kamilov, H. An, and J. Cabello, “Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model.”
    [arXiv:2403.18139]

In Press

  1. Z. Zhang, Y. Hao, X. Jin, D. Yang, U. S. Kamilov, and G. D. Hugo, “Fast motion-compensated reconstruction for 4D-CBCT using deep learning-based groupwise registration,” Biomed. Phys. Eng. Express, in press.
    [10.1088/2057-1976/ad97c1]
  2. Z. Zou, J. Liu, S. Shoushtari, Y. Wang, W. Gan, and U. S. Kamilov, “FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration,” Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV 2024) (Tucson, AZ, February 28-March 3), in press.
    [Project Page] [arXiv:2311.15445]
  3. A. K. Scott, D. M. Fodera, P. Yang, A. Arter, A. M. Hines, S. S. Kolluru, S. G. Zambuto, K. M. Myers, U. S. Kamilov, A. O. Odibo, M. L. Oyen, “Bioengineering approaches for patient-specific analysis of placenta structure and function,” Placenta, in press.
    [10.1016/j.placenta.2024.08.005]

Recent Publications

  1. S. Shoushtari, J. Liu, E. P. Chandler, M. S. Asif, and U. S. Kamilov, “Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis,” Proc. Int. Conf. Mach. Learn. (ICML 2024) (Vienna, Austria, July 21-27), pp. 45154-45182.
    [Project Page] [arXiv:2310.00133] [code]
  2. Y. Hu, S. V. V. N. Kothapalli, W. Gan, A. L. Sukstanskii, G. F. Wu, M. Goyal, D. A. Yablonskiy, U. S. Kamilov, “DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model,” Proc. Int. Symp. Biomedical Imaging 2024 (ISBI 2024) (Athens, Greece, May 27-30).
    [10.1109/ISBI56570.2024.10635694] [arXiv:2311.18073]
  3. Y. Hu, M. Delbracio, P. Milanfar, and U. S. Kamilov, “A Restoration Network as an Implicit Prior,” Proc. Int. Conf. Learn. Represent. (ICLR 2024) (Vienna, Austria, May 7-11).
    [Project Page] [arXiv:2310.01391] [code]
  4. J. Hu, W. Gan, Z. Sun, H. An, and U. S. Kamilov, “A Plug-and-Play Image Registration Network,” Proc. Int. Conf. Learn. Represent. (ICLR 2024) (Vienna, Austria, May 7-11).
    [Project Page] [arXiv:2310.04297] [code]
  5. M. Renaud, J. Liu, V. de Bortoli, A. Almansa, and U. S. Kamilov, “Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models,” Proc. Int. Conf. Learn. Represent. (ICLR 2024) (Vienna, Austria, May 7-11).
    [Project Page] [arXiv:2310.03546] [code]
  6. Y. Hu, W. Gan, C. Ying, T. Wang, C. Eldeniz, J. Liu, Y. Chen, H. An, and U. S. Kamilov, “SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction,” Magn. Reson. Med., vol. 92, no. 3, pp. 1048-1063, September 2024.
    [Project Page] [10.1002/mrm.30121] [arXiv:2210.02584] [code]

Notable Publications

  1. C. Park, S. Shoustari, W. Gan, and U. S. Kamilov, “Convergence of Nonconvex PnP-ADMM With MMSE Denoisers,” Proc. Int. Workshop on Computational Advances in Multi-Sensor Adaptive Process. (CAMSAP 2023) (Los Suenos, Costa Rica, December 10-13), pp. 511-515.
    [Project Page] [10.1109/CAMSAP58249.2023.10403463] [CAMSAP 2023 Student Paper Award finalist]
  2. 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.
    [10.1109/tsp.2012.2217334] [arXiv:1105.6368] [IEEE SPS Best Paper Award 2017]
  3. 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.
    [10.1364/optica.2.000517] [Nature “News and Views”]
  4. 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.
    [10.1109/ICASSP.2017.7953313] [ICASSP 2017 Student Paper Award finalist]
  5. 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]
  6. 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%]