Ulugbek S. Kamilov

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Washington University in St. Louis

United States

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Recent Grants

Grant: Close

CAREER: Reconciling Model-Based and Learning-Based Imaging: Theory, Algorithms, and Applications

Open Date: 2021-07-01

Close Date: 2026-06-30

Grant: Close

CIF: Small: Collaborative Research: Signal Processing for Nonlinear Diffractive Imaging: Acquisition, Reconstruction, and Applications

Open Date: 2018-07-01

Close Date: 2021-06-30

Grant: Close

Spline methods for the continuous/discrete processing of images

Open Date: 2008-10-01

Close Date: 2012-09-30

Articles (18)

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2021

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2021

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2021

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2020

Collaborators (5)

Philip Bayly

Professor

Washington University in St. Louis

UNITED STATES

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Charles A. Bouman

UNITED STATES

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Pasquale Cascarano

ITALY

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Dmitriy Yablonskiy

Professor

Washington University in St. Louis

UNITED STATES

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Hongyu An

Professor

Washington University in Saint Louis School of Medicine

UNITED STATES

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Ulugbek S. Kamilov

Research Interests

Explore related searches

Contact this professor

Recent Grants

Articles (18)

SPICER: Self‐supervised learning for MRI with automatic coil sensitivity estimation and reconstruction

Constrained Regularization by Denoising With Automatic Parameter Selection

Domain Expansion via Network Adaptation for Solving Inverse Problems

PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction

Self-Supervised Deep Equilibrium Models With Theoretical Guarantees and Applications to MRI Reconstruction

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

Deep Equilibrium Learning of Explicit Regularization Functionals for Imaging Inverse Problems

Self-Supervised Deep Equilibrium Models With Theoretical Guarantees and Applications to MRI Reconstruction

Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks

Learning‐based motion artifact removal networks for quantitative R2∗ mapping

Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields

Deep Model-Based Architectures for Inverse Problems Under Mismatched Priors

Deformation-Compensated Learning for Image Reconstruction Without Ground Truth

CoIL: Coordinate-Based Internal Learning for Tomographic Imaging

SGD-Net: Efficient Model-Based Deep Learning With Theoretical Guarantees

Scalable Plug-and-Play ADMM With Convergence Guarantees

Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact‐free and denoised images

Collaborators (5)

Philip Bayly

Charles A. Bouman

Pasquale Cascarano

Dmitriy Yablonskiy

Hongyu An

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