This library implements several forms of histogram equalization/selection/matching. This includes classical and several forms of exact histogram equalization. Additionally, several measurements of contrast enhancement are provided.

Most functions support:

• numpy or cupy (i.e. GPU-based) arrays
• nd images (not just typical 2D images)
• image masks for only working on subsets of images if they take an image
• a number of bins or a complete histogram if they take either

import hist
h = hist.imhist(im) # get the histogram of an image
out = hist.histeq(im, 256) # applies classical histogram equalization using 256 bins (deafult is 64 to match MATLAB)
out = hist.histeq_exact(im) # applies exact histogram equalization using 256 bins (default) with VA method
out = hist.histeq_exact(im, method='LM') # applies exact histogram equalization using 256 bins (default) with LM method

See the documentation for histeq_exact for which methods are supported, references to the relevant papers, and additional parameters.

The histeq and histeq_exact methods will be GPU-accelerated if passed a cupy array:

im_gpu = cupy.asarray(im)
h_gpu = hist.imhist(im_gpu)
out_gpu = hist.histeq(im_gpu, 256)
out_gpu = hist.histeq_exact(im_gpu)

The code can be run as a standalone program:

python3 -m hist input.png output.png
python3 -m hist input # converts a 3d image stored in a folder

See python3 -m hist --help for more information.

References for individual exact strict-ordering and metric algorithms is in function documentation.

This is the code is the implementation of the algorithms discussed and enhanced in the following papers:

• ... (coming soon)