针对实际应用中所采集的图像对比度低、边缘细节模糊的问题，提出了基于非下采样 Contourlet 变换 ( NSCT ) 、模糊集、人工蜂群 ( ABC ) 优化的自适应图像增强方法 . 首先对输入图像进行 NSCT 分解，得到一个低频子带和多个高频子带；然后依据贝叶斯萎缩阈值和非线性增益函数增强高频子带系数，采用模糊增强法增强低频子带系数，并利用 ABC 算法优化其中的模糊参数，以提高模糊增强法的自适应性；接着用低频子带图像的信息熵作为 ABC 算 法的适应度函数，同时引入较劣种群随机初始化策略改进 ABC 算法，以缩短增强方法的运行时间 . 文中采用该增强方法对淡水鱼、铁轨表面、储粮害虫 3 类图像进行了
增强实验，并依据主观视觉效果和对比度增益、清晰度增益、信息熵 3 个客观定量评价指标，对文中方法及其他 3 种同类增强方法进行了比较 . 结果表明，所提出的方法视觉效果最佳，能提高图像的对比度和清晰度，目标边缘光滑，且增加了图像的信息量，便于后续准确地进行图像检测与识别.

Proposed in this paper is an adaptive image enhancement method on the basis of nonsubsampled Contourlet transform (NSCT) ， fuzzy sets and artificial bee colony (ABC) optimization ， which helps improve the low contrast and definition of the image acquired in practical applications. In this method ， first ， an input image is decomposed into a low-frequency sub-band and several high-frequency sub-bands through NSCT. Secondly ，the coefficients of high-frequency sub-bands are enhanced according to Bayesian shrinkage threshold and nonlinear gain function ， while that of the low-frequency sub-band is enhanced by using the fuzzy enhancement method with its adaptability improved by fuzzy parameter optimization via ABC algorithm. Then ， for the purpose of reducing running time ， the entropy of low-frequency sub-band image is used as the fitness function of ABC algorithm and a random initializing strategy of inferior populations is introduced to improve ABC algorithm. The proposed enhancement method is finally employed to process three kinds of images of freshwater fish ， rail surface and grain pest ， and a comparison is made between the proposed method and three other similar enhancement methods in terms of subjective visual effect and such objective quantitative evaluation indices as contrast gain ， definition gain and entropy. Experimental results show that the proposed method is of the most
excellent visual effect because it helps obtain images with improved contrast and definition ， smooth edge and greater information amount ， which benefits further accurate image detection and recognition.