11 citations · 18 across the 6 of their papers we have counts for
6 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…
Semantic Segmentation and Scene Reconstruction of RGB-D Image Frames: An End-to-End Modular Pipeline for Robotic Applications
Zhiwu Zheng, Lauren Mentzer, Berk Iskender +3
Robots operating in unstructured environments require a comprehensive understanding of their surroundings, necessitating geometric and semantic information from sensor data. Tradit…
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,…
Improving Dense Contrastive Learning with Dense Negative Pairs
Berk Iskender, Zhenlin Xu, Simon Kornblith +2
Many contrastive representation learning methods learn a single global representation of an entire image. However, dense contrastive representation learning methods such as DenseCL…
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…
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…