Abstract
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.
Highlights
1.1 Problem formulation The contrast of an image is one of the most important factors influencing its subjective quality
We show that a natural extension of the logarithmic image processing (LIP) model is to use the recently developed symmetric LIP (SLIP) model
Compared with the other algorithms, we can see that the proposed algorithm produces results visually quite close to that of the local color correction (LCC) and parametric log-ratio (PLR) algorithms
Summary
1.1 Problem formulation The contrast of an image is one of the most important factors influencing its subjective quality. An image, which is subjectively rated as low contrast, is usually associated with a limited dynamic range. Pixels of an image can be broadly classified as either in the areas of shadow, mid-tone, or highlight. They correspond to pixels in the lower end, middle part, and the higher end of the histogram, respectively. Pixels of an image can be distributed mostly in the shadow and highlight areas which have limited dynamic ranges. Such an image is classified as local low contrast. The first three are typical cases of images with global low contrast, while the other three are typical cases of local low contrast
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