Search Results for author:
Found 7 papers, 5 papers with code
Digital autofocusing of a coded-aperture Laue diffraction microscope
To provide optimal depth resolution with a coded-aperture Laue diffraction microscope, an accurate position of the coded-aperture and its scanning geometry need to be known.
CodEx: A Modular Framework for Joint Temporal De-blurring and Tomographic Reconstruction
The method is a synergistic combination of a novel acquisition method with a novel non-convex Bayesian reconstruction algorithm.
Joint ptycho-tomography with deep generative priors
In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.
Distributed optimization for nonrigid nano-tomography
Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist beam damage during data acquisition.
Near, far, wherever you are: simulations on the dose efficiency of holographic and ptychographic coherent imaging
Different studies in x-ray microscopy have arrived at conflicting conclusions about the dose efficiency of imaging modes involving the recording of intensity distributions in the near (Fresnel regime) or far (Fraunhofer regime) field downstream of a specimen.
Image and Video Processing Applied Physics Medical Physics
Three dimensions, two microscopes, one code: automatic differentiation for x-ray nanotomography beyond the depth of focus limit
Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering.
Image and Video Processing Applied Physics Optics
TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks
We present \TOMOGAN{}, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions.
