气象预报中阴雨雾图像优化识别仿真

Simulation and Optimization of Rain Fog Image in Weather Forecast

对气象预报中的阴雨雾图像的识别,能够更好的识别低分辨率图像中有用的但是没有表现出来的信息。对低分辨率图像的优化识别,需要获取图像整体色度差均值,将图像目标区域的轮廓变得光滑,完成对阴雨雾图像的识别。传统方法先提取图像的相位和幅值特征,将直方图定义为图像的特征向量,但忽略了获取图像整体色度差均值,导致图像识别精度较低。提出基于二次最大熵的模糊聚类的阴雨雾图像识别方法。利用多尺度Retinex变换理论对图像的亮度进行增强,将低分辨率图像分割为目标区域和非目标区域,获取图像整体色度差均值,给出低分辨率图像的最大判断准则,利用形态学滤波将图像目标区域的轮廓变得光滑,完成对阴雨雾低分辨率图像优化识别。实验结果表明,所提方法识别精度高,可以有效地识别出阴雨雾低分辨率图像中的重要信息。

In this paper, we propose a recognition method for cloudy, rainy, and foggy image based on fuzzy clus- tering of secondary maximum entropy. Firstly, multi-scale Retinex conversion theory was used to enhance brightness of image, and image with low resolution was divided into object region and non-object area. Then, mean value of o- verall color difference of image was acquired, and maximum judgment criteria of image with low resolution were pro- vided. Moreover, morphological filter was used to turn outline of the object area into smooth. Finally, the optimiza- tion recognition was completed. The experimental resuhs show that the method has high recognition precision and can recognize important information in cloudy, rainy, and foggy image with low resolution effectively.