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The mean brightness of each ROI is determined using numerical values assigned to grey-scale pixels.
where E(X) and E Y) are the mean brightness of the images X and Y, respectively.
Chao Zuo et al. [12] propose range limited bi-histogram equalization (RLBHE) that preserves the mean brightness of the image.
where B and B s are the mean brightness of the original and modified image after using the smoothing algorithm, respectively.
The simple method to find out the separated point is to test all possible gray-scale values from 0 to L−1 of the histogram by calculating the difference between the mean brightness of input and the mean brightness of output.
Finally, in order to minimize the difference between the mean brightness of the output image and the original image, the modified histogram is normalized by the equation below [7]: f n ( x ) = B B s f s ( x ) (12).
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The purpose of this approach is to minimize each sub-histogram error corresponding to its mean brightness for histogram equalization.
The mean brightness values of each square were obtained by superposing a rectangular tool of 50×50 pixels over each measurement patch and using the eyedropper tool; results were reported on the histogram X-axis of the picture as mean±SD.
The dependent variable was represented by all the brightness scores calculated as the differences between the mean brightness values of white badge vs. background and white badge vs. body.
Mean brightness is an indicator of the illumination level.
Overall,[22] uses local phase coherence, mean brightness level, and variance of the HVS frequency response and contrast as additional inputs, together with the earlier mentioned four methods, to various ANN models designed for quality estimation.
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