问题描述
我正在尝试从图像中删除高斯噪声。我自己添加了噪音：
I'm trying to remove a Gaussian noise from an image. I've added the noise myself using:
nImg = imnoise(img,'gaussian',0,0.01);
我现在需要使用自己的过滤器消除噪音，或者至少减少噪音。理论上，据我所知，使用 ones（3）/ 9 的卷积矩阵应该有助于并使用像这样的高斯卷积矩阵[1 2 1 ; 2 4 2; 1 2 1] / 9 或 fspecial（'gaussian'，3）应该更好。然而，他们真的不能很好地做到这一点：$ b $ b
I now need to remove the noise using my own filter, or at least reduce it. In theory, as I understand, using a convolution matrix of ones(3)/9 should help and using a Gaussian convolution matrix like [1 2 1; 2 4 2; 1 2 1]/9 or fspecial('gaussian',3) should be better. Yet, they really don't do the trick so well:
我错过了一些重要的事情吗？顺便说一句，我需要使用卷积。
Am I missing something important? I need to use convolution, by the way.
推荐答案
你没有遗漏任何东西！
显然，你不能完全消除噪音。您可以尝试不同的过滤器，但所有过滤器都需要权衡：
You are not missing anything!Obviously, you can't remove the noise completely. You can try different filters, but all of them will have a tradeoff:
如果您通过以下方式考虑这一点会变得更加明显：
It becomes more obvious if you think of this in the following way:
但在现实生活中，图像中有很多物体。因此，当您应用卷积时，通过混合来自不同相邻对象的像素会导致模糊。
But in real life, there are many objects in the image. Thus, when you apply the convolution you cause blur by mixing pixels from different adjacent objects.
还有更复杂的去噪方法，如：
There are more sophisticated denoising methods like:
- 中位数去噪
- 双边过滤器
- 基于模式匹配的去噪
他们没有使用仅卷积。顺便说一句，即使他们也不能做魔法。
They are not using only convolution. By the way, even they can't do magic.
这篇关于如何从MATLAB中删除图像中的高斯噪声？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！