Histogram brightness equalization is a simple color transformation process, that will make all color brightnesses distribute roughly equally.
Histogram brightness equalization demonstrates interesting results when transforming pictures having skew color distribution. It is demonstrated in the following 3 pictures.
The 3 pictures are taken at the same wooden surface. The ordinary picture is quite dark, and it is quite difficult to recognize textures on the surface. When it is transformed into gray scale, it is even difficult to see. But when transformed into brightness balanced, all textures become readily recognizable.
Histogram brightness balance tries to make the originally lightly skewed brightness distribution uniform. Therefore, is stretches the narrow color ranges in the ordinary picture to the whole brightness range, the result is that the colors look very different.
Histogram brightness balance can be a preprocess before image recognition process, because it makes edges more recognizable.
However, histogram balance can also introduce problems. At the same time of stretching decent pixel colors, it also stretches noises. So in a dark scene, a histogram brightness balanced picture can look very noisy. A simple remedy would be to use a digital filter to smooth pixels at a cost of slower responses.