General Purpose Machine Learning Tool-Kit for Bragg Coherent Diffraction Imaging

Deep learning has emerged as a powerful alternative to the iterative phase retrieval approach, that can provide robust reconstruction of Fourier-space diffraction pattern data where iterative methods often fail to solve the phase retrieval problem. Although emphasis to date has focussed on inversion from Fourier-space to real-space images, the process of recovering real-space images remains unclear due to the inherent and currently intractable complexity of deep learning methods. In this project you will develop Physics-Aware SuperResolution convolutional neural network tools to enhance the visibility of Fourier-space diffraction patterns thus enabling rapid and accurate reconstruction of phase information. This project is a collaboration between the Ada Lovelace Institute, the Diamond Light Source and the University of Southampton.