基于均衡化与同态滤波的光学图像去云模型

近年来对太阳耀斑的研究受到大多数研究人员的青睐,但获得的影像资料容易受到云层影响。在图像处理中,云雾的存在不仅使太阳耀斑信息被削弱,影响准确性,而且对太阳耀斑的分析、处理、识别和预测造成严重影响。因此,太阳耀斑图像中云雾的去除显得十分必要,对后期的研判、分析及预测都有着重要的意义。首先对原始图像进行像素值提取,并进行高斯降噪、椒盐降噪处理和小波分析,分解高低频信号。在此基础上,建立了基于高频分量直方图均衡化,低频分量同态滤波处理的小波变换模型来进行去云处理。同时,将新模型与未经分解的图像进行同态滤波、小波去云进行对比去云效果,并对其进行分析,组合去云处理的去云效果较明显。针对该算法,通过改变反射参数、边缘锐度等4个指标进行鲁棒性检验。

In recent years, research on solar f lares has been favored by most researchers, but the images obtained are susceptible to cloud imagery. In image processing, the presence of clouds not only weakens the solar f lare information, but also has image accuracy, and it has a serious impact on the analysis, processing, recognition and prediction of solar f lares. Therefore, the removal of clouds in the solar f lare image is very necessary, and it has important significance for the later research, analysis and prediction. Firstly, the original image is extracted with pixel values, and Gaussian noise reduction, salt and pepper noise reduction processing and wavelet analysis are performed to decompose the high and low frequency signals. On this basis, a wavelet transform model based on high-frequency component histogram equalization and low-frequency component homomorphic filtering is established to perform de-cloud processing. At the same time, the new model and the undecomposed image are homomorphic filtered, and the wavelet is compared with the cloud to compare the cloud effect, and the cloud effect is combined. For the algorithm, the robustness test is carried out by changing the four parameters of ref lection parameters and edge sharpness.