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Fundamentally, diffusion process is an iterated one, starting with a poor quality image, and converging to a completely blurred mean-value image, with no significant structure left.
This is not dissimilar to the ability of a radiologist to interpret a poor quality image successfully (albeit less confidently, perhaps).
The patients with immune-compromised conditions, patients with CT without sufficient slices or poor quality image to analyze the maxillary sinus and patients with CT that did not allow analyzing the maxillary teeth were excluded.
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Real but poor quality images are here.
Using quality assessment metric cannot, however, be a panacea for the recognition of poor quality images.
Poor quality images will be recorded but may be omitted from our analysis.
The aim is to increase contrast of plate-like regions to avoid missing plate location especially in poor quality images.
Table 1 shows these results that are the proof of performance of our algorithm even for poor quality images.
To improve performance it makes sense to imitate the recognition process in humans, who succeed in recognising faces even from poor quality images.
The combination of active deformation and shallow glacial outwash sediments results in particularly complicated seismic data, such that application of relatively standard processing schemes yields only poor quality images.
Magnetic Resonance Imaging (MRI) allows high quality explorations if implants are made from non-magnetic alloys like titanium but it provides poor quality images with Chromium-Cobalt alloys, commonly used in TKA, (Sutter et al. 2013).
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