Feature extraction via similarity search: application to atom finding and denoising in electron and scanning probe microscopy imaging

Suhas Somnath,Miaofang Chi,Albina Borisevich,Stephen Jesse,Nicholas Cross,Sergei V Kalinin,Gerd Duscher,Christopher R Smith

doi:10.1186/s40679-018-0052-y

Abstract

We develop an algorithm for feature extraction based on structural similarity and demonstrate its application for atom and pattern finding in high-resolution electron and scanning probe microscopy images. The use of the combined local identifiers formed from an image subset and appended Fourier, or other transform, allows tuning selectivity to specific patterns based on the nature of the recognition task. The proposed algorithm is implemented in Pycroscopy, a community-driven scientific data analysis package, and is accessible through an interactive Jupyter notebook available on GitHub.

Highlights

Recent advances in transmission electron microscopy (STEM) and scanning probe microscopy (SPM) made atomically resolved imaging of solids and surfaces routine [1,2,3,4,5]
The fast Fourier transform (FFT) of these images shows that the cleaned image only captures the signal from a few lower order peaks while information from several higher order peaks are lost
The current stateof-art technique for image denoising is a non-local means (NLM) [31] technique called block-matching and 3D filtering (BM3D) [32], which identifies windows or patches that are similar, performs 3D wavelet denoising on similar patches and applies a Wiener filter

Summary

Introduction

Recent advances in (scanning) transmission electron microscopy (STEM) and scanning probe microscopy (SPM) made atomically resolved imaging of solids and surfaces routine [1,2,3,4,5]. Until recently atomically resolved images were used solely to establish the local structure of materials and make qualitative observations on its Somnath et al Adv Struct Chem Imag (2018) 4:3. These applications necessitate the development of robust and reliable techniques to extract atomic coordinates from atomically resolved images, requiring little or no human supervision. These generally require a combination of feature extraction methods with physics-based deconvolution. We further note that in certain cases, the proposed algorithm leads to physically significant decompositions—we defer these studies to follow-on work focused on specific studies of imaging phenomena

Methods

Results

Discussion

Conclusion