基于改进人工鱼群的模糊图像自适应增强算法

Fuzzy Image Adaptive Enhancement Algorithm Based on Improved Artificial Fish Populations

为妥善处理模糊图像对比度低、细节不够清晰等问题,增强图像目标识别效果,提出一种基于改进人工鱼群的模糊图像自适应增强算法。处理像素点与物方点坐标,通过边缘数据参考序列与对比序列得到图像纹理信息,凭借图像独立分量特征去除模糊图像噪声,在图像颜色系统中引入混合高斯模型,划分目标图像与背景图像;构建模糊隶属度函数,归一化图像灰度值,计算像素点平均值与协方差,融合人工鱼群算法和Powell算法搜索隶属度函数参数,通过线性反向转换将模糊域图像映射到灰度域,完成图像增强任务。仿真结果表明,所提算法能有效抑制噪声影响,可在增强后模糊图像内得到清晰准确的特征目标点信息,实用性强。

In order to solve the problems of low contrast and insufficient details of fuzzy images and enhance the effect of target recognition, this paper puts forward an adaptive enhancement algorithm for fuzzy images based on an improved artificial fish swarm. Firstly, the coordinates of pixel and object space were processed. And then the texture information of the image was obtained through the reference sequence and contrast sequence of edge data. Secondly, the noise was removed from the blurred image through image independent component features, and then the Gaussian mixture model was introduced into a color system to divide the target image and background image. Thirdly, a fuzzy membership function was constructed, and the gray value of the image was normalized. After that, the mean value and covariance of pixels were calculated. Moreover, the artificial fish swarm algorithm and Powell algorithm were used to search the parameters of membership functions. Furthermore, the image of the fuzzy domain was mapped to the gray domain through linear inverse transformation. Finally, the image enhancement was completed. Simulation results prove that the proposed algorithm can effectively suppress the noise and get clear and accurate feature point information in the enhanced image, so it has strong practicability.