8 February 2010 Efficient implementation of kurtosis based no reference image sharpness metric
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Abstract
The sharpness of an image is a function of its spectral density. Wider spectrum implies sharper image. Thus the image sharpness can be measured by measuring the shape of its spectrum. Bivariate kurtosis can be used to measure the shape and shoulder of a two dimensional probability distribution. It is known that the low frequencies correspond to the slowly changing components of an image and high frequencies correspond to faster gray level changes in the image, which gives information about the finer details such as edges. When an image is in focus, the high frequency components are maximized to define the edges sharply. Thus kurtosis, which measures the width of the shoulder of the probability distribution, corresponding to the high frequencies, can be used to measure the sharpness. This work presents efficient low complexity architecture of kurtosis based image sharpness no reference metric. The calculation of higher order moments is a computational intensive task that involves a large number of additions and multiplications. A recursive IIR filter based implementation of the moments is proposed using a cascade of single pole filters. The conducted simulation results show clearly the reduction in computation while maintaining the same accuracy.
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Rony Ferzli, Lakshmi Girija, Walid S. Ibrahim Ali, "Efficient implementation of kurtosis based no reference image sharpness metric", Proc. SPIE 7532, Image Processing: Algorithms and Systems VIII, 75320E (8 February 2010); doi: 10.1117/12.843733; https://doi.org/10.1117/12.843733
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