$3D$ Crystal Image Analysis based on Fast Synchrosqueezed Transforms

Abstract

We propose an efficient algorithm to analyze $3D$ atomic resolution crystal images based on a fast $3D$ synchrosqueezed wave packet transform. The proposed algorithm can automatically extract microscopic information from $3D$ atomic resolution crystal images, e.g., crystal orientation, defects, and deformation, which are important information for characterizing material properties. The effectiveness of our algorithms is illustrated by experiments of synthetic datasets and real $3$D microscopic colloidal images.

References in corpus (4)

• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
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• ConceFT: Concentration of Frequency and Time via a multitapered synchrosqueezed transform
• Combining $2D$ synchrosqueezed wave packet transform with optimization for crystal image analysis