A generalized gamma correction algorithm based on the SLIP model

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.