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W is amplitude contrast ratio, which denotes contribution of image contrast that result from inelastic scattering in the image formation that is dominated by elastic electron scattering, and γ(k) is phase shift produced by spherical aberration and defocus that can be described by the Scherzer formula (Williams and Carter 1996), γ k = π 2 C s λ 3 k 4 − 2 Δ zλ k 2 (3).
Recently, pictorial models of object representation which emphasise the differential contribution of image features at several hierarchically organised stages of classification have emerged [42].
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In terms of 'commitment', that is, the amount of meaning potential that is committed in the process of instantiation (Painter and Martin 2011; Painter et al. 2013), the analysis has highlighted the important contribution of images to impart attitudinal meanings, in particular via facial expressions and body stance.
Prior to this time, the contribution of retinal image acceleration or target acceleration to smooth pursuit is modest, at best [16], [25].
The approach taken here was to assess the relative contribution of various image features to fixation selection.
A variance components model was used to explore the contribution of subject, image (within subject), observer and reading (within observer) to overall variability.
To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features).
Because there is only one late image and usually more early images available per year, the overall contribution of the late image is much higher because of duplicate occurrences resulting from the combination of expansion and generalization.
Figure 7 Maximal theoretical contribution of each LDR image in the overall SNR of the reconstructed HDR image and the influence of the weighting function.
However, different locations have different contributions to HVS' perception of image [11].
In this perspective, DeHCA optical image processing may play a role in breast assessment in combination with US, although the additional diagnostic contribution of DeHCA optical image processing to that of US alone deserves further investigation.
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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