What Information Is Lost by Histogram Equalization?

The loss in information is simply the number of bits necessary to count the different images that have the same processed image (i.e. after histogramm normalization).

1. What is the effect of histogram equalization?
2. What are the disadvantages of histogram equalization?
3. When would histogram equalization not be appropriate to use?
4. Where does histogram equalization fail?

What is the effect of histogram equalization?

Histogram Equalization is a computer image processing technique used to improve contrast in images . It accomplishes this by effectively spreading out the most frequent intensity values, i.e. stretching out the intensity range of the image.

What are the disadvantages of histogram equalization?

So in theory, if the histogram equalization function is known, then the original histogram can be recovered. The calculation is not computationally intensive. A disadvantage of the method is that it is indiscriminate. It may increase the contrast of background noise, while decreasing the usable signal.

When would histogram equalization not be appropriate to use?

A simple histogram equalization may fail for certain image intensity distributions. In your case there are some bins with excessive number of pixels. For other methods, try for example gamma and contrast corrections.

Where does histogram equalization fail?

Histogram equalization fails when the input image (a) has a large area low-intensity background. In this case, the histogram (d) has a spike component corresponding to the background graylevel.