Search Results for author:
Found 7 papers, 6 papers with code
GLIMPSE: Generalized Local Imaging with MLPs
Deep learning is the current de facto state of the art in tomographic imaging.
Deep Injective Prior for Inverse Scattering
Unlike supervised methods that necessitate both scattered fields and target permittivities, our method only requires the target permittivities for training; it can then be used with any experimental setup.
FunkNN: Neural Interpolation for Functional Generation
Our answer is FunkNN -- a new convolutional network which learns how to reconstruct continuous images at arbitrary coordinates and can be applied to any image dataset.
Deep Variational Inverse Scattering
Inverse medium scattering solvers generally reconstruct a single solution without an associated measure of uncertainty.
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering
In this paper, we introduce an implicit neural representation-based framework for solving the inverse obstacle scattering problem in a mesh-free fashion.
Conditional Injective Flows for Bayesian Imaging
Most deep learning models for computational imaging regress a single reconstructed image.
Trumpets: Injective Flows for Inference and Inverse Problems
We propose injective generative models called Trumpets that generalize invertible normalizing flows.
