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We use the blur level of the defocused images to calibrate blur difference and camera parameters.
Another question is how to estimate the blur level (measured by a function B) in an image.
Figure 2 shows how the blur level of an example texture image is increased during blur equalization.
By equalizing the blur level, a high degree of invariance can be achieved without losing too much distinctiveness, which is of high relevance for practical usage.
Fig. 3 Experiment 2, mean performance thresholds (blur level or noise level at which participants achieved 75% accuracy) for older and younger participants.
Fig. 2 From left to right, the increased blur level during blur equalization is shown using an example texture image [27].
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Training is done using original (non-blurred) images and evaluation is done with different Gaussian blur levels.
We generate images on nine different Gaussian blur levels, leading from (theoretically) σ=0, indicating the original image, to σ=4.
These approaches apply specific concepts during feature extraction to ignore information which changes between differing blur levels.
However, if considering lower blur levels (up to a kernel size of 5×5 pixels), which are probably more relevant in practice, the level of invariance is still worthwhile.
The difference between the two age groups was not significant at any of the remaining blur levels, including the no-blur condition (all p values > 0.08, BF 10 < 1.15).
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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