Histogram matching
Encyclopedia
Histogram matching is a method in image processing
of color adjustment
of two images using the image
histograms
.
It is possible to use histogram matching to balance detector responses as a relative detector calibration technique. It can be used to normalize two images, when the images were acquired at the same local illumination (such as shadows) over the same location, but by different sensors, atmospheric conditions or global illumination.
of the two images' histograms -
for the reference image and 
for the target image. Then for each gray level 
, we find the gray level 
for which 
, and this is the result of histogram matching function: 
. Finally, we apply the function 
on each pixel of the reference image.
Image processing
In electrical engineering and computer science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image...
of color adjustment
Color mapping
Color mapping is a function that maps the colors of one image to the colors of another image. A color mapping may be referred to as the algorithm that results in the mapping function or the algorithm that transforms the image colors...
of two images using the image
Image
An image is an artifact, for example a two-dimensional picture, that has a similar appearance to some subject—usually a physical object or a person.-Characteristics:...
histograms
Image histogram
An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. 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...
.
It is possible to use histogram matching to balance detector responses as a relative detector calibration technique. It can be used to normalize two images, when the images were acquired at the same local illumination (such as shadows) over the same location, but by different sensors, atmospheric conditions or global illumination.
The algorithm
Given two images, the reference and the adjusted images, we compute their histograms. Following, we calculate the cumulative functionsCumulative distribution function
In probability theory and statistics, the cumulative distribution function , or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. Intuitively, it is the "area so far"...
of the two images' histograms -
for the reference image and for the target image. Then for each gray level , we find the gray level for which , and this is the result of histogram matching function: . Finally, we apply the function on each pixel of the reference image.