Joint denoising and line distortion correction for raster-scanned image series

TL;DR

This paper introduces a model for correcting spatial distortions and noise in raster-scanned imaging data, specifically applied to electron microscopy, enhancing data accuracy through joint denoising and distortion correction.

Contribution

It proposes a novel approach for modeling and correcting position distortions in sequential data acquisition, improving image quality in electron microscopy.

Findings

Different models of position noise are compared.

Numerical implementations demonstrate improved correction accuracy.

Application to STEM and HAADF data shows practical benefits.

Abstract

The problem of noise in a general data acquisition procedure can be resolved more accurately if it is based on a model that describes well the distortions of the data including both spatial and intensity changes. The focus of this article is the modeling of the position distortions during sequential data acquisitions. A guiding example is the data obtained by Scanning Transmission Electron Microscopy (STEM) and High Angular Annular Dark Field (HAADF) data, in particular. The article discusses different models of the position noise and their numerical implementations comparing some computational results.