[db:标题]

在Halcon中有这样一个函数： 　　estimate_noise estimate_noise — Estimate the image noise from a single image. 　　Signature 　　　　estimate_noise(Image : : Method, Percent : Sigma) 　　Description 　　The operator estimate_noise estimates the standard deviation of additive noise within the domain of the image that is passed in Image. The standard deviation is returned in Sigma. 　　　　The operator is useful in the following use cases: 　　　　determination of MinContrast for matching, 　　　　determination of the amplitude for edge filters, 　　　　camera evaluation, 　　　　monitoring errors in camera operation (e.g., user overdrives camera gain). 　　即从单幅图像中评估图像噪音的均方差，这个算子可以用于计算匹配时的最小对比度（发现新大陆了，原路模板匹配还可以用这个做自动化）、边缘检测滤波器的幅度、摄像机评估、控相机操作中的错误（例如用户过度调节相机增益）。 我觉得还可以把他作为自动去噪的一个参考指标。 　　Halcon里提供了四个评估噪音的方法：: 'foerstner', 'immerkaer', 'least_squares', 'mean'，其本身最推荐的方法是immerkaer，如其帮助文档里所说： 　　Use the method 'immerkaer', instead of the methods 'foerstner', 'least_squares', or 'mean'. The method 'immerkaer' does not rely on the existence of homogeneous image regions, and hence is almost always applicable. 　　关于immerkaer方法，开放的Halcon基本上提供了完整的算法思路： 　　　　'immerkaer': If Method is set to 'immerkaer', first the following filter mask is applied to the input image: 　　　　The advantage of this method is that M is almost insensitive to image structure but only depends on the noise in the image. Assuming a Gaussian distributed noise, its standard deviation is finally obtained as where N is the number of image pixels to which M is applied. Note that the result obtained by this method is independent of the value passed in Percent. 　　这个M算子明显就是类似一个边缘检测的算子，然后把所有这个算子的结果相加，再求某个意义下的平均值，Halcon说这个方法的好处是对图像的结构不敏感，而只完全依赖于图像的噪音本身。 　　我想有了这个提示，要实现这个功能应该就是很简单的过程了。