Exact histogram equalization and specification

Perform exact histogram specification or equalization of 2D grayscale images
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Aktualisiert 11 Dez 2021

Exact Histogram Specification & Equalization of Grayscale Images

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Histogram equalization is a traditional image enhancement technique which aims to improve visual appearance of the image by assigning equal number of pixels to all available intensity values. Histogram specification is a generalization of histogram equalization and is typically used as a standardization technique to normalize image with respect to a desired probability mass function or properties such as mean intensity, energy and entropy. Unlike classical histogram specification, exact histogram specification algorithm implemented here is able to modify the histogram of any image almost exactly (see snapshot).

The .m file exact_histogram.m is an implementation of an exact histogram specification algorithm described in:

[1] Coltuc D. and Bolon P., 1999, "Strict ordering on discrete images and applications", Proceedings 1999 International Conference on Image Processing

[2] Coltuc D., Bolon P. and Chassery J-M., 2006, "Exact histogram specification", IEEE Transcations on Image Processing 15(5):1143-1152

For a quick demo enter demoHS into Matlab command window.

To access additional information regrading function enter: help exact_histogram

License

MIT © 2019 Anton Semechko a.semechko@gmail.com

Zitieren als

Anton Semechko (2024). Exact histogram equalization and specification (https://github.com/AntonSemechko/exact_histogram), GitHub. Abgerufen .

Kompatibilität der MATLAB-Version
Erstellt mit R2008a
Kompatibel mit allen Versionen
Plattform-Kompatibilität
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Versionen, die den GitHub-Standardzweig verwenden, können nicht heruntergeladen werden

Version Veröffentlicht Versionshinweise
1.0.1.1

Use README.md from GitHub

1.0.1.0

- migrated to GitHub

1.0.0.0

Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.
Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.