How To Make A Histogram On Word
Histogram Matching
How to generate a histogram for an image, how to equalize the histogram, and finally how to alter your image histogram to be like to another histogram.
The code is available here on Github.
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What is an image histogram?
Before offset defining the histogram, for simplicity, we utilise grayscales images. Then later I explicate the process for the color images likewise.
The image histogram indicates the intensity distribution of an i mage. In other words, the paradigm histogram shows the number of pixels in an image having a specific intensity value. As an case, presume a normal image with pixel intensities varies from 0 to 255. In order to generate its histogram we merely need to count the number of pixels having intensity value 0, then 1 and go along to the 255. In Fig.1, we have a sample v*5 image with pixel diversities from 0 to 4. In the first step for generating the histogram, we create the Histogram Table, by counting the number of each pixel intensities. Then we can easily generate the histogram past creating a bar nautical chart based on the histogram table.
How to generate an image histogram?
In python, nosotros can use the post-obit two functions to create and and so display the histogram of an epitome.
Virtually of the time when we create a histogram, nosotros normalize the histogram by dividing the number of pixels with each intensity value by the normalizing factor which is the multiplication of the image width and epitome height. For ease of apply, if the input epitome of the generate_histogram office is a color image, we get-go convert to a grayscale image(see line# 6).
How to equalize an image histogram?
Histogram equalization is commonly used in lodge to enhance the contrast of the image. Accordingly, this technique can't guarantee to e'er meliorate the quality of the paradigm. Calculating CDF (cumulative distributive function) is a common manner to equalizing the histogram of an image. In Fig.two, we have calculated the CDF for the sample paradigm that we created in Fig.1. Furthermore, in Fig.3 nosotros show the equalized histogram of the previous sample.
In order to calculate the equalized histogram in python, I have created the following codes:
Hither is 3 different epitome taken past me and used every bit examples. As shown in Fig.four, for the first prototype the histogram shows that the number of pixels with low intensity is more than the brighter pixels. The situation is totally opposite for the 2nd prototype, where the density of the brighter pixels is much more than than the darker ones. The 3rd image seems to have a semi-normal histogram.
Enhancing images using equalized histogram
As mentioned, we can modify the contrast level of an prototype using its equalized histogram. As shown in Code.2, line #12, for each pixel in an input image, we tin use its equalized value. The results might be better than the original epitome, merely it is not guaranteed. In Fig.5, we depict the modified version of the 3 images. Equally shown, modifying the images using their equalized histogram results in images with a higher level of contrast. This feature tin be useful in many computer vision tasks.
What is the histogram matching?
Presume nosotros have two images and each has its specific histogram. Then we want to respond this question before going further, is it possible to alter ane epitome based on the contrast of some other one? And the answer is Yes. In fact, this is the definition of the histogram matching. In other words, given images A, and B, it is possible to change the contrast level of A co-ordinate to B.
Histogram matching is useful when we want to unify the dissimilarity level of a group of images. In fact, Histogram equalization is also can be taken equally histogram matching, since nosotros alter the histogram of an input image to be similar to the normal distribution.
In social club to match the histogram of images A and B, we need to kickoff equalize the histogram of both images. And so, nosotros need to map each pixel of A to B using the equalized histograms. And so we alter each pixel of A based on B.
Let's clarify the above paragraph using the post-obit example, in Fig.6.
In Fig.6, we accept image A as the input image and Prototype B as the target image. We want to modify the histogram of A, based on the distribution of B. In the first step, nosotros calculate both histogram and the equalized histogram of both A, and B. Then we demand to map each pixel of A, based on the value of its equalized histogram to the value of B. And then, for case for pixels with the intensity level of 0 in A, the corresponding value of A equalized histogram is four. Now, we have a expect at the B equalized histogram and detect the intensity value corresponding to 4, which is 0. So we map the 0 intensity from A to 0 from B. We continue for all intensity values of A. If there is no map from the equalized histogram of A to B, we just need to pick the nearest value.
I have implemented the above process in Python as well
Fig.7 shown an example of histogram matching. Equally you see, while the leftmost prototype is a vivid prototype, the center image tin exist considered a better image in terms of the contrast level. So, we decided to modify the leftmost using the contract of the heart image. The result, which is the rightmost image has been improved.
Conclusion
In this article, I explained histogram matching which is a useful method while we cope with the images. I first started by explaining how to generate an image histogram. Then, how to equalize the generated histogram, and finally how to change a film based on the dissimilarity level of some other picture show, called histogram matching. The code with the explanation is as well available on Github.
The code is available here on Github.
Delight clap if you similar the post.
Source: https://towardsdatascience.com/histogram-matching-ee3a67b4cbc1
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