Scanning electron microscopes (SEMs) are used in neuroscience and materials science to image square centimeters of sample area at nanometer scales. Since imaging rates are in large part SNR-limited, imaging time is proportional to the number of measurements taken of each sample; in a traditional SEM, large collections can lead to weeks of around-the-clock imaging time. We previously reported a single-beam sparse sampling approach that we have demonstrated on an operational SEM for collecting “smooth” images. In this paper, we analyze how measurements from a hypothetical multi-beam system would compare to the single-beam approach in a compressed sensing framework. To that end, multi-beam measurements are synthesized on a single-beam SEM, and fidelity of reconstructed images are compared to the previously demonstrated approach. Since taking fewer measurements comes at the cost of reduced SNR, image fidelity as a function of undersampling ratio is reported.
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