Sentence examples for mean brightness error from inspiring English sources

MMBEBHE recursively partitions the image histogram into multiple groups based on mean brightness error (MBE).

Afterwards, minimum mean brightness error bi-histogram equalization (MMBEBHE) was suggested [26] to reach a greater level of brightness preservation without revealing the unwanted artifacts by using a minimum absolute mean brightness error (AMBE) function.

Chen and Ramli [15] propose to select the threshold minimizing the absolute mean brightness error between the input and the resulting images.

Moreover, the BPWDRHE was estimated for gray-scale and color images and then compared to the others in various aspects with some common quantitative assessments, such as the absolute mean brightness error, the discrete entropy, and the measure of enhancement.

A similar measure has been used in [58] called the absolute mean brightness error (AMBE) which measures the deviation of the mean intensity of the enhanced image (m c ) from the mean intensity of the original image (m o ).

An extended version of BBHE and DSIHE is the minimum mean brightness bi-histogram equalization (MMBEBHE) [10] where input image histogram is recursively separated into multiple sub-histograms using absolute mean brightness error (AMBE).

The purpose of this approach is to minimize each sub-histogram error corresponding to its mean brightness for histogram equalization.

In particular, the minimization of the within-class variance is similar to the minimization of the total squared error of each sub-histogram, and it corresponds to the mean brightness.

Calculate the mean brightness within the region.

Mean brightness is an indicator of the illumination level.

These include the mean brightness, entropy, contrast, and gradient.