一种改进的直方图均衡化图像增强方法

分析了直方图均衡化算法的基本原理,指出图像部分灰度级尤其是表征图像细节的灰度级被过度合并是导致处理后图像细节信息丢失而变得模糊的原因。针对此不足,提出了一种均衡化处理后增加图像灰度级的方法。首先在空间域或者频域中,提取出原图像的高频细节成分,然后将其与原图像直方图均衡化处理后的结果进行叠加,并且可以通过调整高频成分的权值系数获得细节得到不同程度增强后的图像。实验结果表明：与传统直方图均衡化算法相比,使用该方法处理后的图像既增强了整体对比度,又保留了原图像更多的细节信息。

Image Enhancement Method Using Improved Histogram Equalization

The basic theory of the histogram equalization algorithm is analyzed, the image gray level, especially the gray level that denotes the image details is amalgamated overly, which can cause the imager after processing to lose the details and become fuzzy. In order to solve this problem, an algorithm for the increasing image gray level after the histogram equalization is proposed. Firstly, extract the high frequency details of the original image from the space or the frequency domain, and then add them with the image processed by the histogram equalization, lastly, the different image after enhancement is gained by adjusting the weight coefficient of the high frequency. The experimental results show that comparing with the traditional histogram equalization, the overall contrast can be enhanced and retained the more details of the original image.