187 citations · 705 across the 37 of their papers we have counts for
39 papers
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging
Berk Iskender, Sushan Nakarmi, Nitin Daphalapurkar +2
Dynamic imaging involves the reconstruction of a spatio-temporal object at all times using its undersampled measurements. In particular, in dynamic computed tomography (dCT), only…
Learned Bayesian Cramér-Rao Bound for Unknown Measurement Models Using Score Neural Networks
Hai Victor Habi, Hagit Messer, Yoram Bresler
The Bayesian Cramér-Rao bound (BCRB) is a crucial tool in signal processing for assessing the fundamental limitations of any estimation problem as well as benchmarking within a Bay…
RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging
Berk Iskender, Marc L. Klasky, Yoram Bresler
Dynamic imaging addresses the recovery of a time-varying 2D or 3D object at each time instant using its undersampled measurements. In particular, in the case of dynamic tomography,…
Dynamic Tomography Reconstruction by Projection-Domain Separable Modeling
Berk Iskender, Marc L. Klasky, Yoram Bresler
In dynamic tomography the object undergoes changes while projections are being acquired sequentially in time. The resulting inconsistent set of projections cannot be used directly…
Learning to Bound: A Generative Cramér-Rao Bound
Hai Victor Habi, Hagit Messer, Yoram Bresler
The Cramér-Rao bound (CRB), a well-known lower bound on the performance of any unbiased parameter estimator, has been used to study a wide variety of problems. However, to obtain t…
Scatter Correction in X-ray CT by Physics-Inspired Deep Learning
Berk Iskender, Yoram Bresler
Scatter due to interaction of photons with the imaged object is a fundamental problem in X-ray Computed Tomography (CT). It manifests as various artifacts in the reconstruction, ma…