Overview[ edit ] This method usually increases the global contrast of many images, especially when the usable data of the image is represented by close contrast values. Through this adjustment, the intensities can be better distributed on the histogram. This allows for areas of lower local contrast to gain a higher contrast. Histogram equalization accomplishes this by effectively spreading out the most frequent intensity values. The method is useful in images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to better views of bone structure in x-ray images, and to better detail in photographs that are over or under-exposed.
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In simple terms, it represents the number of pixels for each intensity value considered. In the above figure, X-axis represents the tonal scale black at the left and white at the right , and Y-axis represents the number of pixels in an image.
Here, the histogram shows the number of pixels for each brightness level from black to white , and when there are more pixels, the peak at the certain brightness level is higher. 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. This method usually increases the global contrast of images when its usable data is represented by close contrast values.
This allows for areas of lower local contrast to gain a higher contrast. A color histogram of an image represents the number of pixels in each type of color component.
Adaptive Histogram Equalization Adaptive Histogram Equalization differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.
It is therefore suitable for improving the local contrast and enhancing the definitions of edges in each region of an image. In the case of CLAHE, the contrast limiting procedure is applied to each neighborhood from which a transformation function is derived. CLAHE was developed to prevent the over amplification of noise that adaptive histogram equalization can give rise to. Experimental Results.
More Histogram Equalization The process of adjusting intensity values can be done automatically using histogram equalization. Histogram equalization involves transforming the intensity values so that the histogram of the output image approximately matches a specified histogram. By default, the histogram equalization function, histeq , tries to match a flat histogram with 64 bins, but you can specify a different histogram instead. Notice how this curve reflects the histograms in the previous figure, with the input values mostly between 0. The original image has low contrast, with most pixel values in the middle of the intensity range.
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For eg, brighter image will have all pixels confined to high values. But a good image will have pixels from all regions of the image. So you need to stretch this histogram to either ends as given in below image, from wikipedia and that is what Histogram Equalization does in simple words. This normally improves the contrast of the image. I would recommend you to read the wikipedia page on Histogram Equalization for more details about it.
It quantifies the number of pixels for each intensity value considered. What is Histogram Equalization? To make it clearer, from the image above, you can see that the pixels seem clustered around the middle of the available range of intensities. What Histogram Equalization does is to stretch out this range.