The growing demands of nanoscience require the continuous improvement of visualization methods. The imaging performance of scanning electron microscopy (SEM) is fundamentally limited by the point spread function of the electron beam and degrades because of noise. This paper proposes an auto-optimization algorithm based on deconvolution for the restoration of SEM images. This algorithm uses a constrained least squares filter and does not dependent on the user's experience or the availability of nondegraded images. The proposed algorithm improved the quality of the SEM images of 10-nm Au nanoparticles, and achieved balance among the sharpness, contrast-to-noise ratio (CNR), and image artifacts. For the SEM image of 100-nm pitched line patterns, the analysis of the spatial frequencies allowed the 2.5-fold improvement of the intensity of 4-nm information, and the noise floor decreased approximately 32 times. Along with the results obtained by the application of the proposed algorithm to images of tungsten disulfide (WS2) flakes, carbon nanotubes (CNTs), and HeLa cells, the evaluation results confirm that the proposed algorithm can enhance the SEM imaging of nanoscale features that lie close to the microscope's resolution limit.
Keywords: deconvolution; image processing; image restoration; nanomaterials; point spread function; scanning electron microscopy.
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