Poisson noise removal from high-resolution STEM images based on periodic block matching

Abstract

Scanning transmission electron microscopy (STEM) provides sub-?ngstrom, atomic resolution images of crystalline structures. However, in many applications, the ability to extract information such as atom positions, from such electron micrographs, is severely obstructed by low signal-to-noise ratios of the acquired images resulting from necessary limitations to the electron dose. We present a denoising strategy tailored to the special features of atomic-resolution electron micrographs of crystals limited by Poisson noise based on the block-matching and 3D-filtering (BM3D) algorithm by Dabov et al. We also present an economized block-matching strategy that exploits the periodic structure of the observed crystals. On simulated single-shot STEM images of inorganic materials, with incident electron doses below 4 C/cm ², our new method achieves precisions of 7 to 15 pm and an increase in peak signal-to-noise ratio (PSNR) of 15 to 20 dB compared to noisy images and 2 to 4 dB compared to images denoised with the original BM3D.