大多数原始的遥感影像由于其灰度分布集中在较窄的范围内,影像的细节不够清晰,对比度较低。为了使影像的灰度范围拉开或使灰度均匀分布,从而增大反差,增强影像细节信息,通常采用的方法为直方图均衡化。通过对信息熵定义的阐述,引出直方图均衡化的图像增强算法。通过分析传统直方图均衡化算法中存在的缺陷,进而基于分段映射思想提出一种改进的理想直方图均衡化算法。同时,为了对传统算法和改进算法进行定量化地分析比较,基于同时对比度以及人类视觉对比度分辨率限制和模糊数学的相关思想,分别提出基于加权几何平均数法的合成平均对比度和细节评价参数的定义。最后,采用同时对比度、基于加权几何平均数法的合成平均对比度以及细节评价参数作为定量评价的指标,对所提出的改进算法进行了定量评价。评价结果表明,该改进算法的图像增强效果优于传统的直方图均衡化算法。

The intensity distribution of most original remote sensing images concentrated in a narrow range,resulting in unclear image details and low contrast of the image. Histogram equalization is commonly used to distribute the gray levels of an image uniformg which can increase the contrast of images and enhanced the details. Through the elaboration of the definition of comentropy,we draw forth the image enhancement algorithm of histogram equalization. Then,we put forward an improved algorithm of ideal histogram equalization,which is based on piecewise mapping thoughts through the analysis of the defects existing in the traditional histogram equalization. In addition,we relate and analyze the implementation of the improved algorithm in this paper. Meanwhile,in order to do the quantitative analysis and comparison between the traditional algorithm and the modified algorithm,we use synthetic average contrast,and details evaluation parameters,which are based on simultaneous contrast,the contrast resolution limit,and fuzzy mathematics.Finally,we use simultaneous contrast,synthetic average contrast,and details evaluation parameters as the indexes of quantitative evaluation and quantitative evaluation of the improved algorithm. The results of quantitative evaluation show that the image enhancement effect of the improved algorithm proposed in this paper is better than the algorithm of traditional histogram equalization.