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:

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

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:

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:

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: