# HistogramTransform

HistogramTransform[image]

transforms pixel values of image so that its histogram is nearly flat.

HistogramTransform[image,dist]

modifies pixel values of image so that its histogram matches the probability density function (PDF) of the distribution dist.

HistogramTransform[image,ref]

matches the histogram of image with the histogram of the reference image ref.

HistogramTransform[{x1,x2,},]

transforms values xi.

# Details # Examples

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## Basic Examples(3)

Equalize the histogram of an image:

Match the image histogram with a reference image:

Match histograms of two color images:

## Scope(6)

### Data(5)

Transform a dataset so that it is distributed normally:

Transform multiple datasets so that they are distributed uniformly:

Make the pixel values follow a normal distribution:

Compare the two histograms:

Reshape the image histogram so that each channel follows a normal distribution:

Equalize the histogram of a 3D image:

### Parameters(1)

Reducing the number of quantiles will affect the quality of the transformation function:

## Applications(6)

### Basic Applications(3)

Equalize only the brightness of a color image:

Equalize only the saturation of a color image:

Create an image effect by composing an image and its equalized version:

### Color Transformation(2)

Transfer colors between images:

Perform the transfer in the Lab color space instead:

Colorize a grayscale image by searching for neighborhoods with similar luminance in a color image:

Convert images into a color space where luminance and color information are not correlated:

Normalize the luminance images by reshaping the histogram:

Compute luminance neighborhood statistics and construct a function that gives the color associated to the closest luminance neighborhood:

For each pixel of the grayscale image, create a new pixel by preserving the initial luminance and selecting the nearest color in the reference image:

### Multidimensional Probability Density Function Transfer(1)

Reshape the histogram of a multidimensional dataset by iteratively reshaping random marginal histograms:

Reshape a bivariate dataset to match binormal samples:

Assess the result by visualizing the joint histograms before and after the transfer:

Test whether the transformed data is distributed according to the reference distribution:

Reshape the joint histogram of the hue and the saturation to a circle:

Visualize the joint histograms before and after the transfer:

Create the corresponding result image:

Reshape the 3D joint histogram of an RGB image:

## Properties & Relations(1)

HistogramTransformInterpolation can be used to get the transformation function used in HistogramTransform:

Introduced in 2012
(9.0)
|
Updated in 2014
(10.0)