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'''Histogram equalization''' is a method in [[image processing]] of [[contrast (vision)|contrast]] adjustment using the [[Image histogram|image's histogram]].
Histogram equalization is a specific case of the more general class of histogram remapping methods. These methods seek to adjust the image to make it easier to analyze or improve visual quality (e.g., [[retinex]]).
==Overview==
This method usually increases the global contrast of many images, especially when the image is represented by a narrow range of intensity values. Through this adjustment, the [[luminous intensity|intensities]] can be better distributed on the histogram utilizing the full range of intensities evenly. This allows for areas of lower local contrast to gain a higher contrast. Histogram equalization accomplishes this by effectively spreading out the highly populated intensity values, which tend to degrade image contrast.
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 [[
Histogram equalization often produces unrealistic effects in photographs; however it is very useful for scientific images like [[Thermography|thermal]], [[satellite]] or [[x-ray]] images, often the same class of images to which one would apply [[false-color]]. Also histogram equalization can produce undesirable effects (like visible [[image gradient]]) when applied to images with low [[color depth]]. For example, if applied to 8-bit image displayed with [[List of 8-bit computer hardware palettes|8-bit gray-scale palette]] it will further reduce [[color depth]] (number of unique shades of gray) of the image. Histogram equalization will work the best when applied to images with much higher [[color depth]] than [[Palette (computing)|palette]] size, like [[Continuous function|continuous]] data or 16-bit gray-scale images.
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The '''back projection''' (or "project") of a histogrammed image is the re-application of the modified histogram to the original image, functioning as a look-up table for pixel brightness values.
For each group of pixels taken from the same position from all input single-channel images, the function puts the histogram bin value to the destination image, where the coordinates of the bin are determined by the values of pixels in this input group. In terms of statistics, the value of each output image pixel characterizes the probability that the corresponding input pixel group belongs to the object whose histogram is used.<ref>{{cite
==Implementation==
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