Sentence examples for sharpness values from inspiring English sources

Figure 6 Subjective sharpness values for Datasets I and II.

which denotes the sum of the blur probabilities of all edge blocks with the best sharpness values.

This noise may cause miscalculation of sharpness values, which in turn, degrade the performance of image fusion.

Caviedes and Gurbuz [33] calculated sharpness values using the kurtosis of DCT values from an edge neighborhood.

Figure 6 shows the content specific subjective sharpness values for Datasets I and II, sorted in ascending order.

The candidate blocks shown had the five highest sharpness values (m = 5), as calculated by Equation (3).

For example, the sharpness value of 50 for Content 3 is not the same as the sharpness value of 50 for the other contents.

Marziliano et al. [29] calculated the sharpness value using the edge-intensity profiles after Sobel filtering.

The overall sharpness value is the average value of m correspondence blocks.

It is worth noting that much of the image noise is also related to the high frequencies and may cause miscalculation of sharpness value.

This may be a mean-filter, a min-filter, or a filter ranked between these two, depending on the local sharpness value and the sharpness dependent weighting function selected.