Home Artificial Intelligence Histogram Equalization: A Step-by-Step Guideline (06)

Histogram Equalization: A Step-by-Step Guideline (06)

Histogram Equalization: A Step-by-Step Guideline (06)

Histogram Equalization of Image in Detail

Towards Data Science
Original Image by Dan Fador from Pixabay (Top left image is the foremost image, and the underside left image is the grayscale version of the image. The suitable-side images are the results of Histogram Equalization)


Histogram is the strategy of visual representation of frequency distribution with a bar plot. In computer vision, a picture histogram is the strategy of representation of the frequency of intensity values with a bar plot. With image histogram equalization, we will easily adjust the distribution of frequency values of the image intensities. Generally, the method helps us to extend the contrast and brightness of a picture. The method is easy and simple to implement. This text will discuss the total strategy of histogram equalization together with coding examples.

Table of Contents

  1. Image Histogram
  2. Full strategy of histogram equalization
  3. Step-by-step, hands-on implementation

Image Histogram

Image histogram is the representation of the frequency of image intensity values with a bar plot. In Fig -1, I even have shown a sample image with its intensity values in a 2D space.

The values range from 0 to 7. Let’s calculate the frequency of the values.

An image histogram is a straightforward representation of the frequency against the intensity value with a bar plot, as shown in Fig-3.

Fig-3: Histogram of the Image (Image by Creator)

Full Strategy of Histogram Equalization

Histogram equalization is the strategy of uniformly distributing the frequency of the image intensity values with the assistance of some functions. Mainly the functions are probability function — PDF (Probability Density Function) and CDF (Cumulative Distribution Function).

  • PDF is calculated with the frequency of an intensity value divided by the whole frequency.
  • CDF holds the probability of a probability distribution lower than or equal to a selected value. For instance, PDF of the intensity value 0 → 0.12, 1 → 0.24, 2 → 0.12, etc. So, CDF for 1 is 0.12+0.24 = 0.36, 2 is 0.36+ 0.12=0.48, and so forth. Your entire result’s…


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