Z. Zou, J. Liu, B. Wohlberg, and U. S. Kamilov, “Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems.”
[Project Page] [arXiv:2303.05386]
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.”
S. Shoushtari, J. Liu, and U. S. Kamilov, “DOLPH: Diffusion Models for Phase Retrieval.”
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]
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.”
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]
T. Kerepecky, J. Liu, X. W. Ng, D. W. Piston, U. S. Kamilov, “Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN,” Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. (ICASSP 2023) (Rhodes Island, Greece, June 4–10), in press.
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,” Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. (ICASSP 2023) (Rhodes Island, Greece, June 4–10), in press.
W. Gan, H. Gao, Z. Sun, and U. S. Kamilov, “SINCO: A Novel structural regularizer for image compression using implicit neural representations,” Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. (ICASSP 2023) (Rhodes Island, Greece, June 4–10), in press.
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.
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.
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.
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] [doi:10.1109/msp.2022.3199595] [arXiv:2203.17061]
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.
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]
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%]