反转图像模糊过滤器（不清晰的蒙版） [英] Reverse image blur filter (not unsharp mask)
问题描述
我有原始的位图，我想做高斯模糊逆。
我们称之为invB，高斯模糊作为函数GBlur和原始image O.
Filter我想给invB做输出，所以GBlur（invB）= O。
这个操作与锐化无关图像（我认为它会放弃图像不使它更清晰），永远不会被用来恢复从模糊的原始图像（这不同于假设和可用的数据从这个问题）。
使其短 - 滤波器应该使原始图像反转，在高斯模糊之后给出原始图像。
在InvB中可能有更多的过滤器。
在此先感谢您。
#Edited。
原文：
经过锐化和高斯模糊处理后：
经过高斯模糊然后锐化： p>
$ b $所以我有原始图像，我必须通过高斯模糊（[[1,2,1]，[2,4,2]，[1,2,1]]传递图像， / 16），并在模糊之前准备原始图像，以便它将等于原始图像（只要有可能，最接近原始图像时，不可能）。
也许由于问题性质，我无法通过Google找到任何东西。
请不要通过deblurring解决这个问题 - 它根本没有帮助。
一个去模糊的过滤器，无论是否将其应用于模糊的图像。它可以通过反卷积来实现，也称为反卷积。在傅立叶域中，它等于除以（高斯）滤波器响应而不是相乘。
看看Richardson-Lucy方法 http://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution 。
实际上，由于强烈的噪音增强和振铃，结果往往令人非常失望。为了减少这些影响，维纳过滤器是首选 http://en.wikipedia.org/wiki/Wiener_deconvolution。
I am looking for method to obtain reverse blur filter. I have original bitmap and I want to make something like Gaussian Blur Inverse.
Let us call it invB, and operation of Gaussian blur as function GBlur and original image O. Filter I would like to have gives invB as output, so that GBlur(invB) = O.
This operation has nothing to do with sharpening image (I assume it will disort image not make it sharper) and will never be used to restore original image from blurred one (it differs in assumptions and available data from that problem).
Making it short - Filter should make inverse of original image that will give original image after Gaussian blur. In InvB there are possibly more filters.
Thanks in advance.
#Edited.
Original:
After Sharpen and Gaussian Blur:
After Gaussian Blur then Sharpen:
So I have original image, I have to pass the image through Gaussian Blur ([[1,2,1],[2,4,2],[1,2,1]], / 16) and prepare original image before blurring, so that it will be equal to original image (whenever possible, and closest to the original image when not possible).
Maybe because of the problem nature I cannot find anything via Google. Please, do not tackle this problem by deblurring - it simply does not help.
What you describe is indeed a deblurring filter, whether you apply it to a blurred image or not. It can be implemented by inverse convolution, also called deconvolution. In the Fourier domain, it amounts to dividing by the (Gaussian) filter response instead of multiplying.
Have a look at the Richardson-Lucy method http://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution.
In practice the results are often quite disappointing because of strong noise enhancement and ringing. To reduce these effects, the Wiener filter is preferred http://en.wikipedia.org/wiki/Wiener_deconvolution.
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