New adaptive histogram equalisation heuristic approach for contrast enhancement

Abstract

Contrast enhancement of an image can be performed by using a simple histogram equalisation (HE) technique. However, there are some drawbacks of HE like immense brightness change, artificial effects, over-enhancement, which make it unsuitable to be used in many applications. To resolve these issues a new adaptive heuristic HE approach is proposed in this study. First, probability distribution function (PDF) of the image is calculated. Second, an adaptive parameter is calculated based on the mean and maximum values of that PDF. Thereafter, PDF and cumulative distribution function (CDF) are modified by applying a threshold limit to that adaptive parameter. Finally, another adaptive parameter is finding out by using modified CDF and a new CDF is obtained by using this second adaptive parameter. Traditional HE is then applied with the new CDF to getting the enhanced image. The visual and quantitative results of the proposed method outperform all other state-of-the-art papers and works well both for low and bright contrast images simultaneously. After rigorous experiment, it is concluded that the authors' method enhances the image contrast very well with no over-enhancement or artificial effects in the images and also preserves the original characteristics of the input images.