School of Communication and Information Engineering, Shanghai University, Shanghai, China.
Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology, Huai'an, China.
Comput Intell Neurosci. 2017;2017:6029892. doi: 10.1155/2017/6029892. Epub 2017 Dec 18.
This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly, the normalization method is deployed on intensity levels, and the integration of the processed image with the input image is performed. 100 benchmark images from a public image database named CVG-UGR-Database are used for comparison with other state-of-the-art methods. The experiment results show that the algorithm can not only enhance image information effectively but also well preserve brightness and details of the original image.
本文提出了一种新颖的基于均值和方差的子图像直方图均衡化（MVSIHE）图像增强方法，与基于直方图均衡化（HE）的其他一些方法相比，该方法有效地提高了输入图像的对比度，同时很好地保留了亮度和细节。首先，根据亮度分量的均值和方差将输入图像的直方图分为四个部分，然后分别修改和均衡每个部分的直方图桶。其次，通过连接处理后的子直方图得到结果。最后，在强度级别上部署归一化方法，并将处理后的图像与输入图像进行集成。使用来自名为 CVG-UGR-Database 的公共图像数据库的 100 张基准图像与其他最先进的方法进行比较。实验结果表明，该算法不仅可以有效地增强图像信息，而且可以很好地保留原始图像的亮度和细节。
