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.

中文翻译：

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对比度增强的新的自适应直方图均衡启发式方法

可以通过使用简单的直方图均衡（HE）技术来执行图像的对比度增强。但是，HE还存在一些缺点，例如巨大的亮度变化，人为效果，过度增强，使其不适合在许多应用中使用。为了解决这些问题，本研究提出了一种新的自适应启发式HE方法。首先，计算图像的概率分布函数（PDF）。其次，根据该PDF的平均值和最大值计算自适应参数。此后，通过将阈值限制应用于该自适应参数来修改PDF和累积分布函数（CDF）。最后，通过使用修改后的CDF找出另一个自适应参数，并通过使用第二个自适应参数获得新的CDF。然后将传统的HE与新的CDF结合使用以获取增强的图像。所提方法的视觉和定量结果均优于其他所有最新技术论文，并且对于低对比度和明亮对比度的图像均能很好地发挥作用。经过严格的实验，得出的结论是，作者的方法可以很好地增强图像对比度，而不会在图像中出现过度增强或人为的影响，并且保留了输入图像的原始特征。