11 citations · 24 across the 11 of their papers we have counts for
12 papers
TomoTransformer: Towards a Foundation Model for CT Reconstruction
AmirEhsan Khorashadizadeh, Benjamín Béjar
Supervised deep learning has advanced sparse-view tomographic reconstruction. However, conventional models, which typically map filtered back-projection (FBP) images or sinograms t…
Marginal Flow: a flexible and efficient framework for density estimation
Marcello Massimo Negri, Jonathan Aellen, Manuel Jahn +2
Current density modeling approaches suffer from at least one of the following shortcomings: expensive training, slow inference, approximate likelihood, mode collapse or architectur…
Localized Supervised Learning for Cryo-ET Reconstruction
Vinith Kishore, Valentin Debarnot, AmirEhsan Khorashadizadeh +1
Cryo-electron tomography (Cryo-ET) is a powerful tool in structural biology for 3D visualization of cells and biological systems at resolutions sufficient to identify individual pr…
CryoLithe: Rapid Cryo-ET Reconstruction via Transform-Localized Deep Learning
Vinith Kishore, Valentin Debarnot, AmirEhsan Khorashadizadeh +3
Cryo-electron tomography (cryo-ET) enables 3D visualization of cellular structures. Accurate reconstruction of high-resolution volumes is complicated by the very low signal-to-nois…
LoFi: Neural Local Fields for Scalable Image Reconstruction
AmirEhsan Khorashadizadeh, Tobías I. Liaudat, Tianlin Liu +2
Neural fields or implicit neural representations (INRs) have attracted significant attention in computer vision and imaging due to their efficient coordinate-based representation o…
Glimpse: Generalized Locality for Scalable and Robust CT
AmirEhsan Khorashadizadeh, Valentin Debarnot, Tianlin Liu +1
Deep learning has become the state-of-the-art approach to medical tomographic imaging. A common approach is to feed the result of a simple inversion, for example the backprojection…
Deep Injective Prior for Inverse Scattering
AmirEhsan Khorashadizadeh, Vahid Khorashadizadeh, Sepehr Eskandari +2
In electromagnetic inverse scattering, the goal is to reconstruct object permittivity using scattered waves. While deep learning has shown promise as an alternative to iterative so…
Deep Variational Inverse Scattering
AmirEhsan Khorashadizadeh, Ali Aghababaei, Tin Vlašić +2
Inverse medium scattering solvers generally reconstruct a single solution without an associated measure of uncertainty. This is true both for the classical iterative solvers and fo…
FunkNN: Neural Interpolation for Functional Generation
AmirEhsan Khorashadizadeh, Anadi Chaman, Valentin Debarnot +1
Can we build continuous generative models which generalize across scales, can be evaluated at any coordinate, admit calculation of exact derivatives, and are conceptually simple? E…
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering
Tin Vlašić, Hieu Nguyen, AmirEhsan Khorashadizadeh +1
Implicit representation of shapes as level sets of multilayer perceptrons has recently flourished in different shape analysis, compression, and reconstruction tasks. In this paper,…
Conditional Injective Flows for Bayesian Imaging
AmirEhsan Khorashadizadeh, Konik Kothari, Leonardo Salsi +3
Most deep learning models for computational imaging regress a single reconstructed image. In practice, however, ill-posedness, nonlinearity, model mismatch, and noise often conspir…
Trumpets: Injective Flows for Inference and Inverse Problems
Konik Kothari, AmirEhsan Khorashadizadeh, Maarten de Hoop +1
We propose injective generative models called Trumpets that generalize invertible normalizing flows. The proposed generators progressively increase dimension from a low-dimensional…