Journal

2025

  1. 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]
  2. C. Park, W. Gan, Z. Zou, Y. Hu, Z. Sun, U. S. Kamilov, “Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuning,” J. Math. Imaging Vis., vol. 67, no. 21, 2025.
    [10.1007/s10851-025-01241-1] [arXiv:2311.02003]
  3. 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,” J. Math. Imaging Vis., vol. 67, no. 20, 2025.
    [Project Page] [10.1007/s10851-025-01236-y] [arXiv:2210.06330]
  4. P. Goyes-Penafiel, U. S. Kamilov, and H. Arguello, “CDDIP: Constrained Diffusion-Driven Deep Image Prior for Seismic Data Reconstruction,” IEEE Geosci. Remote Sens. Lett., vol. 22, pp. 1-5, 2025.
    [10.1109/LGRS.2025.3545860] [arXiv:2407.17402]
  5. 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,” vol. 9, no. 4, pp. 412-420, April 2025.
    [10.1109/TRPMS.2025.3528728] [arXiv:2403.18139]
  6. 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, vol. 11, no. 1, p. 015030, 2025.
    [10.1088/2057-1976/ad97c1]

2024

  1. 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]
  2. W. Gan, Q. Zhai, M. T. McCann, C. G. Cardona, U. S. Kamilov, and B. Wohlberg, “PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction,” IEEE Open J. Signal Process., vol. 5, pp. 539-547, 2024.
    [10.1109/OJSP.2024.3375276] [arXiv:2310.07504] [code]
  3. N. Yismaw, U. S. Kamilov, and M. S. Asif, “Domain Expansion via Network Adaptation for Solving Inverse Problems,” IEEE Trans. Comput. Imag., vol. 10, pp. 549-559, 2024.
    [10.1109/TCI.2024.3377101] [arXiv:2310.06235]
  4. P. Cascarano, A. Benfenati, U. S. Kamilov, and X. Xu, “Constrained Regularization by Denoising With Automatic Parameter Selection,” IEEE Signal Process. Lett., vol. 31, pp. 556-560, 2024.
    [10.1109/LSP.2024.3359569]

2023

  1. S. Chen, C. Eldeniz, T. J. Fraum, D. R. Ludwig, W. Gan, J. Liu, U. S. Kamilov, D. Yang, H. M. Gach, and H. An, “Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system,” Med. Phys., vol. 50, no. 10, pp. 6163-6176, October 2023.
    [10.1002/mp.16469]
  2. Z. Zou, J. Liu, B. Wohlberg, and U. S. Kamilov, “Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems,” IEEE Open J. Signal Process, vol. 4, pp. 390-398, 2023.
    [Project Page] [10.1109/ojsp.2023.3296036] [arXiv:2303.05386] [code]
  3. P. Goyes-Penafiel, E. Vargas, C. V. Correa, Y. Sun, U. S. Kamilov, B. Wohlberg, H. Arguello, “Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach,” in IEEE Trans. Geosci. Remote. Sens., vol. 61, pp. 1-12, 2023.
    [10.1109/TGRS.2023.3290468]
  4. 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,” IEEE Trans. Comput. Imag., vol. 9, pp. 796-807, 2023.
    [10.1109/tci.2023.3304475] [arXiv:2210.03837] [code]
  5. 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., vol. 36, no. 5, pp. e4883, May 2023.
    [10.1002/nbm.4883] [biorxiv]
  6. 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., vol. 50, no. 2, pp. 808–820, February 2023.
    [10.1002/mp.16103]
  7. 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., vol. 40, no. 1, pp. 85-97, January 2023.
    [Project Page] [10.1109/msp.2022.3199595] [arXiv:2203.17061]

2022

  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, vol. 3, no. 3, pp. 468-480, September 2022.
    [Project Page] [10.1109/jsait.2022.3220044] [arXiv:2207.13200]
  2. 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] [10.1038/s42256-022-00530-3] [arXiv:2112.00002] [code]
  3. W. Gan, Y. Sun, C. Eldeniz, J. Liu, H. An, and U. S. Kamilov, “Deformation-Compensated Learning for Image Reconstruction without Ground Truth,” IEEE Trans. Med. Imag., vol. 41, no. 9, pp. 2371-2384, September 2022.
    [Project Page] [10.1109/tmi.2022.3163018] [arXiv:2107.05533] [code]
  4. S. Chen, T. J. Fraum, C. Eldeniz, J. Mhlanga, W. Gan, T. Vahle, U. B. Krishnamurthy, D. Faul, H. M. Gach, M. M. Binkley, U. S. Kamilov, R. Laforest, and H. An, “MR-Assisted PET Respiratory Motion Correction Using Deep-Learning Based Short-Scan Motion Fields,” Magn. Reson. Med., vol. 88, no. 2, pp. 676-690, 2022.
    [10.1002/mrm.29233]
  5. 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 for Quantitative R2* Mapping,” Magn. Reson. Med., vol. 88, no. 1, pp. 106-119, 2022.
    [10.1002/mrm.29188] [arXiv:2109.01622] [code]
  6. 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.
    [10.1016/j.jmbbm.2021.105046]

2021

  1. 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.
    [10.1109/tci.2021.3125564] [arXiv:2102.05181] [code]
  2. 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.
    [10.1097/rli.0000000000000792]
  3. Y. Sun, Z. Wu, X. Xu, B. Wohlberg, and U. S. Kamilov, “Scalable Plug-and-Play ADMM with Convergence Guarantees,” IEEE Trans. Comput. Imag., vol. 7, pp. 849-863, July 2021.
    [10.1109/tci.2021.3094062] [arXiv:2006.03224]
  4. J. Liu, Y. Sun, W. Gan, X. Xu, B. Wohlberg, and U. S. Kamilov, “SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees,” IEEE Trans. Comput. Imag., vol. 7, pp. 598-610, June 2021.
    [10.1109/tci.2021.3085534] [arXiv:2101.09379]

2020

  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.
    [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.
    [10.1109/jstsp.2020.2998402] [arXiv:1912.05854] [supplement] [code]
  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.
    [10.1002/mrm.28344] [arXiv:1912.07087] [code]
  4. Y. Sun, J. Liu, and U. S. Kamilov, “Block Coordinate Regularization by Denoising,” IEEE Trans. Comput. Imag., vol. 6, pp. 908-921, 2020.
    [10.1109/tci.2020.2996385] [arXiv:1905.05113] [code]
  5. 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.
    [10.1109/lsp.2020.3006390] [arXiv:2005.07685]
  6. 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.
    [10.1109/lsp.2020.2977214] [arXiv:1907.11793] [code]

2019

  1. Y. Sun, B. Wohlberg, and U. S. Kamilov, “An Online Plug-and-Play Algorithm for Regularized Image Reconstruction,” IEEE Trans. Comput. Imag., vol. 5, no. 3, pp. 395-408, September 2019.
    [10.1109/tci.2019.2893568] [arXiv:1809.04693]
  2. W. Tahir, U. S. Kamilov, and L. Tian, “Holographic particle localization under multiple scattering,” SPIE Adv. Photon., vol. 1, no. 3, p. 036003, May/June 2019.
    [ 10.1117/1.ap.1.3.036003] [arXiv:1807.11812]

2018

  1. H. Mansour, D. Liu, U. S. Kamilov, and P. T. Boufounos, “Sparse Blind Deconvolution for Distributed Radar Autofocus Imaging,” IEEE Trans. Comput. Imag., vol. 4, no. 4, pp. 537-551, December 2018.
    [10.1109/tci.2018.2875375] [arXiv:1805.03269]
  2. E. Bostan, U. S. Kamilov, and L. Waller, “Learning-based Image Reconstruction via Parallel Proximal Algorithm,” IEEE Signal Process. Lett., vol. 25, no. 7, pp. 989-993, July 2018.
    [10.1109/lsp.2018.2833812] [arXiv:1801.09518]
  3. Y. Sun, Z. Xia, and U. S. Kamilov, “Efficient and accurate inversion of multiple scattering with deep learning,” Opt. Express, vol. 26, no. 11, pp. 14678-14688, May 2018.
    [10.1364/oe.26.014678] [arXiv:1803.06594] [code]
  4. H.-Y. Liu, D. Liu, H. Mansour, P. T. Boufounos, L. Waller, and U. S. Kamilov, “SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering,” IEEE Trans. Comput. Imag., vol. 4, no. 1, pp. 73-86, March 2018.
    [10.1109/tci.2017.2764461] [arXiv:1705.04281]

2017

  1. U. S. Kamilov, H. Mansour, and B. Wohlberg, “A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems,” IEEE Signal Process. Lett., vol. 24, no. 12, pp. 1872-1876, December 2017.
    [10.1109/lsp.2017.2763583]
  2. U. S. Kamilov and P. T. Boufounos, “Motion-Adaptive Depth Superresolution,” IEEE Trans. Image Process, vol. 26, no. 4, pp. 1723-1731, April 2017.
    [10.1109/tip.2017.2658944]
  3. U. S. Kamilov, “A Parallel Proximal Algorithm for Anisotropic Total Variation Minimization,” IEEE Trans. Image Process., vol. 26, no. 2, pp. 539-548, February 2017.
    [10.1109/tip.2016.2629449]
  4. S. Rangan, A. K. Fletcher, P. Schniter, and U. S. Kamilov, “Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization,” IEEE Trans. Inf. Theory., vol. 63, no. 1, pp. 676-697, January 2017.
    [10.1109/tit.2016.2619373] [arXiv:1501.01797]

2016

  1. U. S. Kamilov, D. Liu, H. Mansour, and P. T. Boufounos, “A Recursive Born Approach to Nonlinear Inverse Scattering,” IEEE Signal Process. Lett., vol. 23, no. 8, pp. 1052-1056, August 2016.
    [10.1109/lsp.2016.2579647] [arXiv:1603.03768]
  2. U. S. Kamilov and H. Mansour, “Learning optimal nonlinearities for iterative thresholding algorithms,” IEEE Signal Process. Lett., vol. 23, no. 5, pp. 747–751, May 2016.
    [10.1109/lsp.2016.2548245] [arXiv:1512.04754]
  3. U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Trans. Comput. Imag., vol. 2, no. 1, pp. 59–70, March 2016.
    [10.1109/tci.2016.2519261]

2015

  1. 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”]
  2. U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, D. Psaltis, and M. Unser, “Isotropic inverse-problem approach for two-dimensional phase unwrapping,” J. Opt. Soc. Am. A, vol. 32, no. 6, pp. 1092–1100, June 2015.
    [10.1364/josaa.32.001092] [arXiv:1503.04744]

2014

  1. U. S. Kamilov, E. Bostan, and M. Unser, “Variational Justification of Cycle Spinning for Wavelet-Based Solutions of Inverse Problems,” IEEE Signal Process. Lett., vol. 21, no. 11, pp. 1326–1330, November 2014.
    [10.1109/lsp.2014.2334306]
  2. U. S. Kamilov, S. Rangan, A. K. Fletcher, and M. Unser, “Approximate Message Passing with Consistent Parameter Estimation and Application to Sparse Learning,” IEEE Trans. Inf. Theory, vol. 60, no. 5, pp. 2969–2985, May 2014.
    [10.1109/tit.2014.2309005]

2013

  1. E. Bostan, U. S. Kamilov, M. Nilchian, and M. Unser, “Sparse Stochastic Processes and Discretization of Linear Inverse Problems,” IEEE Trans. Image Process., vol. 22, no. 7, pp. 2699–2710, July 2013.
    [10.1109/tip.2013.2255305]
  2. A. Kazerouni, U. S. Kamilov, E. Bostan, and M. Unser, “Bayesian Denoising: From MAP to MMSE Using Consistent Cycle Spinning,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 249–252, March 2013.
    [10.1109/lsp.2013.2242061]
  3. A. Amini, U. S. Kamilov, E. Bostan, and M. Unser, “Bayesian Estimation for Continuous-Time Sparse Stochastic Processes,” IEEE Trans. Signal Process., vol. 61, no. 4, pp. 907–920, February 2013.
    [10.1109/tsp.2012.2226446]
  4. U. S. Kamilov, P. Pad, A. Amini, and M. Unser, “MMSE Estimation of Sparse Lévy Processes,” IEEE Trans. Signal Process., vol. 61, no. 10, pp. 137–147, January 2013.
    [10.1109/tsp.2012.2222394]

2012

  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.
    [10.1109/tsp.2012.2217334] [arXiv:1105.6368] [IEEE SPS Best Paper Award 2017]
  2. U. S. Kamilov, A. Bourquard, A. Amini, and M. Unser, “One-Bit Measurements with Adaptive Thresholds,” IEEE Signal Process. Lett., vol. 19, no. 10., pp. 607–610, October 2012.
    [10.1109/lsp.2012.2209640]
  3. U. S. Kamilov, E. Bostan, and M. Unser, “Wavelet Shrinkage with Consistent Cycle Spinning Generalizes Total Variation Denoising,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 187–190, April 2012.
    [10.1109/lsp.2012.2185929]