Compressive Ptychography using Deep Image and Generative Priors

by   Semih Barutcu, et al.

Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale. It has been extensively used in various areas such as the defense industry or materials science. One major limitation of ptychography is the long data acquisition time due to mechanical scanning of the sample; therefore, approaches to reduce the scan points are highly desired. However, reconstructions with less number of scan points lead to imaging artifacts and significant distortions, hindering a quantitative evaluation of the results. To address this bottleneck, we propose a generative model combining deep image priors with deep generative priors. The self-training approach optimizes the deep generative neural network to create a solution for a given dataset. We complement our approach with a prior acquired from a previously trained discriminator network to avoid a possible divergence from the desired output caused by the noise in the measurements. We also suggest using the total variation as a complementary before combat artifacts due to measurement noise. We analyze our approach with numerical experiments through different probe overlap percentages and varying noise levels. We also demonstrate improved reconstruction accuracy compared to the state-of-the-art method and discuss the advantages and disadvantages of our approach.


page 1

page 5

page 6

page 7

page 8

page 9

page 12


A Deep Generative Approach to Oversampling in Ptychography

Ptychography is a well-studied phase imaging method that makes non-invas...

Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors

Most modern imaging systems involve a computational reconstruction pipel...

Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction

Magnetic resonance imaging of whole fetal body and placenta is limited b...

Robust Compressive Phase Retrieval via Deep Generative Priors

This paper proposes a new framework to regularize the highly ill-posed a...

Learning the Night Sky with Deep Generative Priors

Recovering sharper images from blurred observations, referred to as deco...

WhiteNNer-Blind Image Denoising via Noise Whiteness Priors

The accuracy of medical imaging-based diagnostics is directly impacted b...

RAPToR: A Resampling Algorithm for Pseudo-Polar based Tomographic Reconstruction

We propose a stable and fast reconstruction technique for parallel-beam ...

Please sign up or login with your details

Forgot password? Click here to reset