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A generalized gamma correction algorithm based on the SLIP model

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journal contribution
posted on 2022-05-05, 23:15 authored by Guang DengGuang Deng
Traditional contrast enhancement techniques were developed to enhance the dynamic range of images with narrow histograms. However, it is not unusual that an image with a broad histogram still suffers from low contrast in both the shadow and highlight areas. In this paper, we first develop a unified framework called the generalized gamma correction for the enhancement of these two types of images. The generalization is based on the interpretation of the gamma correction algorithm as a special case of the scalar multiplication of a generalized linear system (GLS). By using the scalar multiplication based on other GLS, we obtain the generalized gamma correction algorithm. We then develop an algorithm based on the generalized gamma correction algorithm which uses the recently developed symmetric logarithmic image processing (SLIP) model. We demonstrate that the proposed algorithm can be configured to enhance both types of images by adaptively choosing the mapping function and the multiplication factor. Experimental results and comparisons with classical contrast enhancement and state-of-the-art adaptive gamma correction algorithms demonstrate that the proposed algorithm is an effective and efficient tool for the enhancement of images with either narrow or broad histogram.


Publication Date



Eurasip Journal on Advances in Signal Processing





Article Number



15p. (p. 1-15)


Springer Open



Rights Statement

© 2016 Deng. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.