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Fig. 5 Efficiency per groups, and per inspected objects.
Fig. 6 Effectiveness per groups, and per inspected objects.
Practitioners provided the inspected objects, and two of them attended the experiment as observers.
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The system rotates an inspected object to recognize the objects in space not on a plane.
The depth of field of the magnification lens does not cover the height of the inspected object.
Firstly, a set of scattering events occurring in the inspected object is determined by means of classical Monte Carlo simulation.
The superresolving process is obtained by the generation of relative movement between the inspected object and the a priori known high-resolution background.
This work presents a methodology to minimize this difficulty by making all planes of the inspected object to cast a resolution within the shorter gap comprised between the object central plane and the detector.
By means of experiments, this paper discusses the behavior of collimators with different shapes and structures from the aspects of detector output signal, mass attenuation coefficient of the inspected object and quality of the actual CT image.
Nevertheless, the variance of the signal associated with each pixel of the detector can be determined, which enables contrast-to-noise ratio (CNR) maps to be computed, in order to predict quantitatively the detectability of defects in the inspected object.
The proposed algorithm, based on a voxel discretization of the inspected object, is presented in detail, as well as its results in simple configurations, which are shown to converge when the sampling steps are chosen sufficiently small.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com