# Overview

Contrast Limited Adaptive Histogram Equalization - adapthist

## Overview

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. It is therefore suitable for improving the local contrast of an image and
bringing out more detail.

However, AHE has a tendency to overamplify noise in relatively homogeneous regions
of an image. A variant of adaptive histogram equalization called contrast limited
adaptive histogram equalization (CLAHE) prevents this by limiting the amplification.


This library is an implementation of the CLAHE algorithm developed by Karel Zuiderveld.

## Installation

python setup.py build_ext -i
python setup.py install


## Notes

• The underlying algorithm relies on an image whose rows and columns are even multiples of

the number of tiles, so the extra rows and columns are left at their original values, thus preserving the input image shape. * For RGB or RGBA images, the algorithm is run on each channel. * For RGBA images, the original alpha channel is removed.