基于模糊熵改进的直方图匹配算法研究

Improved Histogram Matching Algorithm Based on Fuzzy Entropy

以模糊数学为基础,提出了一种基于最大模糊熵改进的直方图匹配图像增强算法,以弥补传统空间域图像增强方法在提高图像对比度时对噪声敏感的缺陷.首先,把灰度图像从空间域映射到模糊域,并以最大模糊熵为基础,将目标图像分为若干个灰度层;然后,针对不同灰度层的特征,用直方图匹配方法为每个灰度层设计相应的匹配函数;最后,用这些匹配函数增强相应的灰度层得到增强后的图像.通过与直方图匹配、直方图均衡化和局部直方图处理算法对比,证明本文算法具有更好的增强效果.本文算法结合模糊熵和直方图匹配算法,可以降低噪声在图像增强中的影响,而且在应用中具有良好的效果.

An improved histogram matching algorithm based on maximum fuzzy entropy is proposed to make up the noise sensitive faultiness of traditional spatial domain methods improving image contrast. Firstly, the gray image is mapped from the spatial domain to the fuzzy domain, and divided into a plurality of gray levels based on maximum fuzzy entropy. Secondly, matching functions that combine characteristics of corresponding gray levels are designed by the histogram matching method. Finally, the functions are used to process corresponding gray levels, and the enhanced image is obtained. By comparison with histo- gram matching, histogram equalization and local histogram processing, the proposed method is shown to have higher enhancement effect. The proposed method that combines maximum fuzzy entropy with histo- gram matching algorithm can reduce the influence of noise in image enhancement and has good effect in ap- plication.