Equalization implies mapping one distribution (the given histogram) to another distribution (a wider and more uniform distribution of intensity values) so the. Histogram equalization is a technique for adjusting image intensities to enhance contrast. normalized histogram of f with a bin for each possible intensity. So. Histogram is a graphical representation of the intensity distribution of an image. In simple terms, it represents the number of pixels for each.

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The cumulative distribution function cdf is shown below.

The MATLAB function computing the mean when we give esualisation a 2 dimensional array our image is returning an average per column. And it seems that it’s pretty well working with the blending ratio I choose. There are many aspects involved in this work and on which to add comments and possible solutions.

Scaling from 1 to preserves the non-zero-ness of the minimum value.

Point Operations – Histogram Equalization

So the final function we produced is taking an image as histohram and providing an histogram with a specified number of bins and range. But this is not the point here because even without we can determine a threshold by looking at the bone Gaussian approximation. On the previous image, we display the original Gaussian pdf computed using the mean and the standard deviation determined with the patches in both areas.

The properties of the CDF allow us to perform such a transform see Inverse distribution function ; it is defined as. Histogrm map these new values you are onto histogram, and you are done.

Histogram equalization

An histogram being a distribution of the number of pixel according to their intensities, we have in this part to analyze histoogram image to determine this distribution.

In this case we can clearly see that some pixels are considered bright while they are dark and vice versa. So all the code I’m going to present here is using matlab programing language syntax. There are two ways to think about and implement histogram equalization, either as image change or as palette change.


Equalisatin we can see on the previous image, we can get a fairly good esualisation of the image but because of the noise, we have a certain number of pixel that are associated to the wrong color. Because a digital image is a discrete set of values that could be seen as a matrix and it’s equivalent to divide each n k by the dimension of the array which is the product of the width by the length of the image.

Project 1 : Histograms

But all algorithm will work with any other languages. Due to the complexity of such a thing I just mention it here as a quick conclusion to say that there exists some solutions. To be able to perform further transformations on the image, we have to normalize the histogram. In this tutorial we will see that how histogram equalization can be used to enhance contrast. This last image is a quite different from the others and I didn’t present it before.

Which could encode different colors. Then we will perform some other operations to allow the user to enter hitogram number of desired bins for the histogram and the range of values that should cover the histogram.

After applying we got the following image and its following histogram. The last thing we need to know that we know the threshold is to create the output image by assigning to each pixel it’s new value.

In the case of CLAHE, the contrast limiting procedure is applied to each neighborhood from which a transformation function is derived. Indeed, we can consider a threshold of which will give us the following result:. Again, pixel values that do not contribute to an increase in the cdf are excluded for brevity. Sign in Get equslisation. So the idea is to get an intermediate image which will be enhanced but not to much so it does not become too bright.


This is indeed due to a programming issue In most cases palette change is better as it preserves the original data. Hstogram aspect is also present on the second image because there equalisafion a noise issue that makes harder the determination of an accurate threshold value.

This cdf shows that the minimum value in the subimage is 52 and the maximum value is The number of possible levels intensity values depends on the numerical type encoding the image.

Histogram Equalization – Towards Data Science

Note that to scale values in the original data that are above 0 to the range 1 to L-1, inclusive, the above equation would instead be: A disadvantage of the method is that equaliswtion is indiscriminate.

So we multiply CDF by 7. But this could be more accurate than the manual selection if the noise has the same standard deviation. With an intensity range going from 0 to for chang. Here is the implementation of this part:.

As we have seen before, some images are really dark and have their histogram concentrated on the lower values of the intensity. The probability of an occurrence of a pixel of level i in the image is.

This allows for areas of lower local contrast to gain a higher contrast. To be able to segment the image, we need to find a threshold that is representing a good value to separate the two different gray level. For instance, a 8 eight bits color images encode the Red and Green channel with three bits and the blue with two.