图像的PSF（点扩散函数）（2D）

本文介绍了图像的PSF（点扩散函数）（2D）的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我是图像分析的新手（使用Python），我想在我的数据（CT扫描）上应用richardson_lucy deconvolution（来自skimage）。出于这个原因，我通过特定软件估算了体素数中的PSF。它的值大约是6.73体素，但我不知道如何将它用作函数中的参数。

I'm new in image analysis (with Python) and I would like to apply the richardson_lucy deconvolution (from skimage) on my data (CT scans). For this reason, I estimated the PSF in "number of voxels" by means of a specific software. Its value is roughly 6.73 voxels, but I don't know how to use it as a paramter in the function.

该函数使用PSF参数作为ndarray，所以我试过这种方式：

The function uses the PSF parameter as a ndarray, so I tried in this way:

from skimage import io from pylab import array img = io.imread ("Slice1.tif") import skimage.restoration as rst PSF = array (6.7) img_dbl = rst.richardson_lucy (img, PSF, iterations=10)

它显示了这个错误： IndexError：数组的索引太多

在CT扫描中，两种不同材料之间的模糊可以链接到高斯PSF。如果您有更多关于去模糊的提示（可能比RL更好），请写下它们。

In CT scans, blurring between two different materials can be linked to a Gaussian PSF. If you have more tips for deblurring (maybe better than RL) just write them.

任何人都可以帮助我。

推荐答案

遇到类似问题并仍在研究中。在我的情况下，如果我没有使用 np.uint8 作为类型，它就不起作用。 CT数据应该是16位，但只使用前12位（映射到[-1024,3096]之间的值。所以我必须将我的图像数据重新缩放到[0-255]，然后才能获得除黑色或白色之外的任何值。

Have a similar problem and still researching it. In my case it didn't work if I didn't use np.uint8 as type. CT data should be 16 bit but only uses the first 12 bits (which are mapped to values between [-1024, 3096]. So I had to rescale my image data to [0-255] before getting anything than black or white out it.

如果我理解正确的话，PSF的总和应该总是1.我可以从你的问题中猜出，你认为点扩散函数是一个高斯有一个有意义的（95％的值？）传播6.7像素。在这种情况下你必须将PSF建模为高斯（这就是我来到这里）。

If I understood that correctly, the sum of the PSF should always be 1. What I can guess from your question is that you assume the point spread function to be a gaussian with a meaningful (95% of values?) spread of 6.7 pixels. In that case you would have to model the PSF as a gaussian (that's what I came here for).

您可以使用@FuzzyDuck在这篇文章中描述的方法创建一个。

You can create one with the method described by @FuzzyDuck in this post.

PSF = gkern(5,2)

这将创建一个高斯5x5内核，其中和1使用@FuzzyDuck提出的方法，sigma为2.请注意，点扩散函数可以应用多次，因此您必须对这些值进行一些实验（或者使用算法来估算那个）。

This would create a gaussian 5x5 kernel with sum 1 using the proposed method by @FuzzyDuck with a sigma of 2. Note that point spread functions could be applied several times so you have to experiment a little bit with the values (or use an algorithm to approximate that).