Compressive Sensing in Transmission Electron Microscopy
Electron microscopy is one of the most powerful tools available in observational science. Magnifications of 10,000,000x have been achieved with picometer precision. At this high level of magnification, individual atoms are visible. This is possible because the wavelength of electrons is much smaller than visible light, which also means that the highly focused electron beams used to perform imaging contain significantly more energy than visible light. The beam energy is high enough that it can cause radiation damage to metal specimens. Reducing radiation dose while maintaining image quality has been a central research topic in electron microscopy for several decades. Without the ability to reduce the dose, most organic and biological specimens cannot be imaged at atomic resolution. Fundamental processes in materials science and biology arise at the atomic level, thus understanding these processes can only occur if the observational tools can capture information with atomic resolution.
The primary objective of this research is to develop new techniques for low dose and high resolution imaging in (scanning) transmission electron microscopy (S/TEM). This is achieved through the development of new machine learning based compressive sensing algorithms and microscope hardware for acquiring a subset of the pixels in an image. Compressive sensing allows recovery of a signal from significantly fewer measurements than total signal size (under certain conditions). The research objective is attained by demonstrating application of compressive sensing to S/TEM in several simulations and real microscope experiments. The data types considered are images, videos, multispectral images, tomograms, and 4-dimensional ptychographic data. In the simulations, image quality and error metrics are defined to verify that reducing dose is possible with a small impact on image quality. In the microscope experiments, images are acquired with and without compressive sensing so that a qualitative verification can be performed.
Compressive sensing is shown to be an effective approach to reduce dose in S/TEM without sacrificing image quality. Moreover, it offers increased acquisition speed and reduced data size. Research leading to this dissertation has been published in 25 articles or conference papers and 5 patent applications have been submitted. The published papers include contributions to machine learning, physics, chemistry, and materials science. The newly developed pixel sampling hardware is being productized so that other microscopists can use compressive sensing in their experiments. In the future, scientific imaging devices (e.g., scanning transmission x-ray microscopy (STXM) and secondary-ion mass spectrometry (SIMS)) could also benefit from the techniques presented in this dissertation.
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