- R. Liu, Y. Sun, J. Zhu, L. Tian, and U. S. Kamilov, “Zero-Shot Learning of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements.”
- W. Gan, Y. Sun, C. Eldeniz, J. Liu, H. An, and U. S. Kamilov, “Deformation-Compensated Learning for Image Reconstruction without Ground Truth.”
- 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.”
- 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,” Magn. Reson. Med., in press.
- 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.
[OpenReview] [arXiv:2106.03668] [Acceptance rate: 2371/9122 = 26%]
- 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.
- Z. Hou, C. A. Guertler, R. J. Okamoto, H. Chen, J. R. Garbow, U. S. Kamilov, and P. V. Bayly, “Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks,” J. Mech. Behav. Biomed. Mater., vol. 126, p. 105046, February 2022.
- Y. Sun, J. Liu, M. Xie, B. Wohlberg, and U. S. Kamilov, “CoIL: Coordinate-based Internal Learning for Tomographic Imaging,” IEEE Trans. Comput. Imag., vol. 7, pp. 1400-1412, November 2021.
- 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., vol. 56, no. 12, pp. 809-819, December 2021.
- 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), pp. 4028-4037.
- 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), pp. 4048-4056.
- 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]
- 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”]
- 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]
- 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]
- 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%]