Gray-scale image brightness-contrast enhancement with multi-model histogram linear contrast stretching (MMHLCS)‎ method

الملخص EN

The purpose of this work is to enhance the contrast of a gray-scale image by using well known histogram stretching algorithm, but, in a new procedure.

The most well known method is the histogram equalization because of its automatic procedure.

However, the effect of brightness saturation will be appeared in some quasi-homogeneous region.

This causes from the merging or adjacent gray level process for flattening the global histogram.

Not only some lower brightness will be grouped together but also some higher brightness in order to uniforms the histogram distribution.

Unlike the method of histogram linear contrast stretching the new wide histogram dynamic range can be assigned directly from the original narrow histogram dynamic range.

However, the histogram of an image can be composed of many models of histogram.

Different models can be represented with different objects.

To enhance each models of histogram independently, the obtained result image will get better visual perceptibility than the global enhancement.

The standard deviation of each model will be calculated and summed for providing the proportional of stretching range.

The resultant image can be provided the higher efficiency in image segmentation or image classification.

Therefore, this paper tries to carry out a method of the multi-model histogram linear contrast stretching.

As each model of histogram can be detected by using the change of eight consecutive signs, each sign is obtained from the difference between two probabilities of adjacent gray level.