No-Reference Weighting Factor Selection for Bimodal Tomography

Bimodal tomography introduces a weighting factor α to incorporate X-ray data into projection images acquired from scanning transmission electron microscope (STEM) for achieving an atom-specific three-dimensional (3D) reconstruction of an object on the nanoscale. Currently its value is chosen by computing reconstructions for a large range of αin(0,1) and comparing them to a hand-segmented ground truth with the mean square error (MSE). Since this is infeasible for an industrial application, in this paper we propose an image quality metric to quantify the quality of tomograms in terms of cross-atomic contamination and noise for selecting the weighting factor without a ground truth. Numerical results demonstrate that our framework can determine close-to-optimal weighting factor within an accuracy of pm 0.03. Moreover, approximating the shape of the minimum by a parabola effectively reduces the computational time by 90%.</p

No-Reference Weighting Factor Selection for Bimodal Tomography

Bimodal tomography introduces a weighting factor α to incorporate X-ray data into projection images acquired from scanning transmission electron microscope (STEM) for achieving an atom-specific three-dimensional (3D) reconstruction of an object on the nanoscale. Currently its value is chosen by computing reconstructions for a large range of αin(0,1) and comparing them to a hand-segmented ground truth with the mean square error (MSE). Since this is infeasible for an industrial application, in this paper we propose an image quality metric to quantify the quality of tomograms in terms of cross-atomic contamination and noise for selecting the weighting factor without a ground truth. Numerical results demonstrate that our framework can determine close-to-optimal weighting factor within an accuracy of pm 0.03. Moreover, approximating the shape of the minimum by a parabola effectively reduces the computational time by 90%.Accepted Author ManuscriptImPhys/Quantitative Imagin