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Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histograms of an image before and after equalization.
Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It 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.
In image processing, histogram matching or histogram specification is the transformation of an image so that its histogram matches a specified histogram. [1] The well-known histogram equalization method is a special case in which the specified histogram is uniformly distributed .
Darktable involves the concept of non-destructive editing, similar to that of some other raw manipulation software. Rather than being immediately applied to raster data of the image, the program keeps the original image data until final rendering at the exporting stage — while parameter adjustments made by a user display in real-time.
The total area of a histogram used for probability density is always normalized to 1. If the length of the intervals on the x-axis are all 1, then a histogram is identical to a relative frequency plot. Histograms are sometimes confused with bar charts. In a histogram, each bin is for a different range of values, so altogether the histogram ...
An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. [1] It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.
Bandwidth equalization, in computer networking; Blind equalization, a digital signal processing technique; Delay equalization; Equalization (communications), specific to communications systems
The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image.