Sparse imaging for fast electron microscopy

Abstract

Scanning electron microscopes (SEMs) are used in neuroscience and materials science to image centimeters of sample area at nanometer scales. Since imaging rates are in large part SNR-limited, large collections can lead to weeks of around-the-clock imaging time. To increase data collection speed, we propose and demonstrate on an operational SEM a fast method to sparsely sample and reconstruct smooth images. To accurately localize the electron probe position at fast scan rates, we model the dynamics of the scan coils, and use the model to rapidly and accurately visit a randomly selected subset of pixel locations. Images are reconstructed from the undersampled data by compressed sensing inversion using image smoothness as a prior. We report image fidelity as a function of acquisition speed by comparing traditional raster to sparse imaging modes. Our approach is equally applicable to other domains of nanometer microscopy in which the time to position a probe is a limiting factor (e.g., atomic force microscopy), or in which excessive electron doses might otherwise alter the sample being observed (e.g., scanning transmission electron microscopy).

Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling

“…Another standard regularization is total variation (TV), i.e., the 1 -norm of the image gradient promoting piecewise constant reconstructed image, as considered in [17] for AFM. The block-DCT representation was coupled with TV for reconstructing SEM data in [4]. The 2 -norm of the image gradient is also widely used as a regularization to promote spatial smoothness and is referred to as the Sobolev energy [11].…”

“…The new generation of direct detection cameras with negligible correlated noise could promote the use of this multi-frame setup with even lower dwell-times. Finally some high resolution acquisitions need to cover large areas (such as in [4] for scanning electron microscopy (SEM)), leading to long acquisition total time, heavy data storage and long processing steps. To increase acquisition speed and/or reduce the full beam exposure, a solution consists in reducing dwell time and subsequently denoising the data as a post-processing operation.…”

“…These results raised a lot of interest toward inverse problems which estimate the image based on partial spatial acquisitions which is referred to as inpainting. It remains an active research area for STEM [8,9] and SEM [4], among others.…”

Reduced-dose and high-speed acquisition strategies for multi-dimensional electron microscopy

“…Random subsampling has also been explored for high-speed electron microscopy data collection, mainly for dose rate considerations. Anderson et al simulated such an experiment in the scanning electron microscope (SEM) by selecting a random subset of pixel locations and recovering the full frame image by interpolation, using image smoothness as a prior knowledge [24]. This approach was also simulated on HR-STEM images of grain boundaries and lower magnification STEM images of extremely beamsensitive materials, with the scanning of only 5% of the total number of pixels in the original image and recovery achieved by Bayesian dictionary learning technique [25].…”

Compressed sensing for dose reduction in STEM tomography

“…Alternatively, one can scan only a fraction of the pixels following a certain downsampling pattern, e.g. uniform or random (Figure 1d-e) [13,14].…”