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An example of histogram matching. 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. [2]
Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images. There are two ways to think about and implement histogram equalization, either as image change or as palette change.
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.
Histogram equalization is a popular example of these algorithms. Improvements in picture brightness and contrast can thus be obtained. In the field of computer vision, image histograms can be useful tools for thresholding. Because the information contained in the graph is a representation of pixel distribution as a function of tonal variation ...
An example of an algorithm that employs the statistical properties of the images is histogram matching. This is a classic algorithm for color transfer, but it can suffer from the problem that it is too precise so that it copies very particular color quirks from the target image, rather than the general color characteristics, giving rise to ...
Maybe it makes sense to mention Histogram Matching (match histogram to a distribution of a 2nd source). Even more important is Histogram Equalization in log-log-domain (Histogram Hyperbolization). Hyperbolization is achieved by using the power function for the cdf.
In image processing, normalization is a process that changes the range of pixel intensity values. Applications include photographs with poor contrast due to glare, for example.
Histogram equalization is a non-linear transform which maintains pixel rank and is capable of normalizing for any monotonically increasing color transform function. It is considered to be a more powerful normalization transformation than the grey world method.