Image Contrast Enhancement (ICE) is a crucial step in several image processing and computer vision applications. Its main objective is to improve the quality of the visual information contained in the processed images. The presence of noise and small sets of pixels in images are not only irrelevant for their visualization. It also negatively affects the improvement process of ICE schemes since the inclusion of irrelevant information avoids the appropriate distribution of significant pixel intensities in the enhanced image. As a consequence of this effect, most of the proposed ICE methods present different associated problems such as the production of undesirable artifacts, noise amplification, over saturation and bad human visual perception. In this paper, an Image Contrast Enhancement (ICE) method for grayscale and color images is presented. The proposed approach has the propriety of eliminating noisy and irrelevant information in order to improve the distribution capacity of significant pixel intensities in the enhanced image. Our method eliminates multiple groups of a very small number of pixels that, according to their characteristics, do not represents any object or important detail of the image. This process is done by the Mean-shift algorithm, which is used to replace such sets of irrelevant pixels in the original histogram by significant pixel densities represented by local maxima. Then, the Moth Swarm Algorithm (MSA) is used to redistribute the pixel intensities of the reduced histogram so that the value from Kullback–Leibler entropy (KL-entropy) has been maximized. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image also improving its human visual appearance.

图像对比度增强（ICE）是几种图像处理和计算机视觉应用程序中的关键步骤。其主要目的是提高处理后图像中包含的视觉信息的质量。图像中的噪声和少量像素集的存在不仅与其可视化无关。这也对ICE方案的改进过程产生了负面影响，因为包含无关信息会避免增强图像中有效像素强度的适当分布。由于这种效果，大多数建议的ICE方法都存在不同的相关问题，例如产生不希望的伪影，噪声放大，饱和度过高以及不良的人类视觉感知。本文提出了一种用于灰度和彩色图像的图像对比度增强（ICE）方法。所提出的方法具有消除噪声和无关信息的适当性，以提高增强图像中有效像素强度的分布能力。我们的方法消除了非常少数量的像素的多个组，根据它们的特征，它们不代表图像的任何对象或重要细节。该过程由均值漂移算法完成，该算法用于用局部最大值表示的有效像素密度替换原始直方图中的此类不相关像素集。然后，使用蛾群算法（MSA）重新分配缩小后的直方图的像素强度，以使来自Kullback-Leibler熵（KL熵）的值最大化。已经考虑了文献中常用的不同公共数据集对提出的方法进行了测试。还将其结果与其他知名ICE技术产生的结果进行比较。对实验结果的评估表明，所提出的方法突出了图像的重要细节，也改善了其人的视觉外观。