Abstracts Book - IMRC 2018

• SD2-O012 Invited Talk
LEARNING PTYCHOGRAPHIC RECONSTRUCTION WITH
BACKPROPAGATION
Youssef Nashed 1
1 Argonne National Laboratory, Mathematics and Computer Science, United States.
Synchrotron radiation light source facilities are leading the way to ultrahigh
resolution X-ray imaging. High resolution imaging is essential to understanding
the fundamental structure and interaction of materials at the smallest length
scale possible. Diffraction based methods achieve nanoscale imaging by
replacing traditional objective lenses by pixelated area detectors and
computational image reconstruction. Among these methods, ptychography is
quickly becoming the standard for sub-30 nanometer imaging of extended
samples, but at the expense of increasingly high data rates and volumes.
We present our work for solving the ptychographic image reconstruction
problem through fitting a physics based model to the measured data. The model
parameters are learned in a similar manner to deep neural networks, utilizing
the backpropagation method as implemented in Google TensorFlow package.
This approach has advantages in terms of speed and accuracy compared to
current state of the art algorithms, and demonstrates re-purposing the deep
learning backpropagation algorithm to solve general inverse problems that are
prevalent in materials imaging research.
Keywords: Image Reconstruction, Inverse Problems, Automatic Differentiation
Presenting authors email: ynashed@anl.gov