Abstract
AbstractOne of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (TEM) experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integrated into a single camera frame during the acquisition process, and then extracted upon readout using statistical CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.Mathematics Subject Classification: (2010) 94A08 · 78A15
Highlights
In-situ transmission electron microscopy (TEM) has established itself as a very powerful analytical technique for its ability to provide a direct insight into the nature of materials under a broad range of environmental conditions
The results show the efficacy of the compressive sensing (CS) approach to TEM video
The simulation used real TEM video and sampled it according to the coded aperture compressive temporal imaging (CACTI) scheme
Summary
In-situ transmission electron microscopy (TEM) has established itself as a very powerful analytical technique for its ability to provide a direct insight into the nature of materials under a broad range of environmental conditions. With the recent development of a wide range of insitu TEM stages and dedicated environmental TEM, it is possible to image materials under high-temperature, gas, and liquid conditions, as well as in other complex electrochemical, optical, and mechanical settings [1,2,3,4]. In many of these applications, it is often critical to capture the dynamic evolution of the microstructure with a very high spatial and temporal resolution. The limitation in implementing this technology (or any other hardware-based acquisition system), is that as the frame rates increase, reading out the images becomes a challenge—the issue becomes a data transfer problem rather than an electron detection problem
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