Purpose of histogram equalization

Histogram equalization is a method to process images in order to adjust the contrast of an image by modifying the intensity distribution of the histogram. The objective of this technique is to give a linear trend to the cumulative probability function associated to the image.

1. What is the advantage of histogram equalization in image processing?
2. Can histogram equalization always provide a better result state your reasons?
3. How is histogram equalization performed?
4. What are the disadvantages of histogram equalization?

What is the advantage of histogram equalization in image processing?

After normalizing each sub-histogram, finally, the image was equalized. A major advantage of QDHE is that it's enhanced the image without any intensity saturation, noise amplification, and over-enhancement.

Can histogram equalization always provide a better result state your reasons?

Histogram equalization is used to enhance contrast. It is not necessary that contrast will always be increase in this. There may be some cases were histogram equalization can be worse. In that cases the contrast is decreased.

How is histogram equalization performed?

Histogram equalization can be done in three steps [1]: Compute the histogram of the image. Calculate the normalized sum of histogram. Transform the input image to an output 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.