blocks|key|210781|text|根据medianBlur()文档，它在内部使用BORDER_REPLICATE处理边框像素。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|210782|BORDER_REPLICATE+
Python:+cv.BORDER_REPLICATE

aaaaaa%7Cabcdefgh%7Chhhhhhh+|code-block|syntax|javascript|210783|因此，它重复边缘像素，直到所有像素都是窗口的中间像素。|210784|编辑:要应用大小为5x5的内核，第一个像素应该位于图像的第三行和第三列。它意味着将边框复制两个像素。所以你的形象在内部变成这样：|210785|++[[11,+11,+11,++4,+17,++1,++5,++5,++5],
+++[11,+11,+11,++4,+17,++1,++5,++5,++5],
+++[11,+11,+11,++4,+17,++1,++5,++5,++5],
+++[+6,++6,++6,+14,++0,+12,+16,+16,+16],
+++[24,+24,+24,+19,+13,+18,+23,+23,+23],
+++[+7,++7,++7,+11,+11,+10,++5,++5,++5],
+++[10,+10,+10,+13,+23,++3,++0,++0,++0],
+++[10,+10,+10,+13,+23,++3,++0,++0,++0],
+++[10,+10,+10,+13,+23,++3,++0,++0,++0]]|210786|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.opencv.org/3.4.9/d4/d86/group__imgproc__filter.html#ga564869aa33e58769b4469101aac458f9^0|2|C|N|G|2|C|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]]|D|@$9|13|A|14|1|15]]|E|$]]|$1|F|3|G|5|H|7|16|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|17|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|18|8|@]|D|@]|E|$]]|$1|O|3|P|5|H|7|19|8|@]|D|@]|E|$I|J]]|$1|Q|3|-4|5|6|7|1A|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X]]]]

According to the <a href="https://docs.opencv.org/3.4.9/d4/d86/group__imgproc__filter.html#ga564869aa33e58769b4469101aac458f9" rel="nofollow noreferrer"><code>medianBlur()</code></a> documentation, it uses <code>BORDER_REPLICATE</code> internally to process border pixels.

<pre><code>BORDER_REPLICATE 
Python: cv.BORDER_REPLICATE

aaaaaa|abcdefgh|hhhhhhh 
</code></pre>

So it repeats the bordering pixels until all pixels are the mid pixel of a window.

EDIT: To apply a kernel with a size of 5x5, the first pixel should be at the third row and column of the image. It means replicate the the border by two pixels. So your image becomes this internally:

<pre><code> [[11, 11, 11, 4, 17, 1, 5, 5, 5],
 [11, 11, 11, 4, 17, 1, 5, 5, 5],
 [11, 11, 11, 4, 17, 1, 5, 5, 5],
 [ 6, 6, 6, 14, 0, 12, 16, 16, 16],
 [24, 24, 24, 19, 13, 18, 23, 23, 23],
 [ 7, 7, 7, 11, 11, 10, 5, 5, 5],
 [10, 10, 10, 13, 23, 3, 0, 0, 0],
 [10, 10, 10, 13, 23, 3, 0, 0, 0],
 [10, 10, 10, 13, 23, 3, 0, 0, 0]]
</code></pre>

blocks|key|3745007|text|来自文件：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3745008|3745009|中值滤波器在内部使用#BORDER_REPLICATE来处理边框像素，参见#BorderTypes|blockquote|3745010|3745011|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|J|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|K|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|L|8|@]|9|@]|A|$]]|$1|G|3|-4|5|6|7|M|8|@]|9|@]|A|$]]]|H|$]]

From documentation:

<blockquote>
 The median filter uses #BORDER_REPLICATE internally to cope with
 border pixels, see #BorderTypes
</blockquote>

blocks|key|1223067|text|这里，函数cv2.medianBlur()计算内核窗口下所有像素的中值，中心像素被替换为这个中值。这是非常有效的消除盐和胡椒噪音。值得注意的是，在高斯滤波器和盒滤波器中，中心元素的滤波值可以是原始图像中可能不存在的值。然而，中值滤波的情况并非如此，因为中心元素总是被图像中的某个像素值所取代。这有效地降低了噪音。内核大小必须是正数整数。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1223068|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Here, the function cv2.medianBlur() computes the median of all the pixels under the kernel window and the central pixel is replaced with this median value. This is highly effective in removing salt-and-pepper noise. One interesting thing to note is that, in the Gaussian and box filters, the filtered value for the central element can be a value which may not exist in the original image. However this is not the case in median filtering, since the central element is always replaced by some pixel value in the image. This reduces the noise effectively. The kernel size must be a positive odd integer.

I've been working with a grayscale image that has a lot of Salt and Pepper noise and came to know that MedianBlur is very useful. So I used the Python version of Opencv(<code>cv2.medianBlur()</code>). It worked but didn't work in the way I wanted. So I was looking for the actual algorithm it worked on and got the following explanation:

If you have a kernel size(k) of 5, then for every 5(row count)x5(column count) square window, the central pixel of this window will get replaced by the Median value of all the elements in it. So, for instance, consider this window:

<pre><code> [[11, 4, 17, 1, 5],
 [ 6, 14, 0, 12, 16],
 [24, 19, 13, 18, 23],
 [ 7, 11, 11, 10, 5],
 [10, 13, 23, 3, 0]] 
</code></pre>

Here, the central element, 13 will get replaced by the Median of all these elements, i.e. 11. Is this correct? If yes, what happens to the first 2 rows, since there can't be elements in the first two rows which are the central element for ANY window? As per my observation(comparing the original and processed image), the first two rows change too.

How does Opencv MedianBlur actually work?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我一直在处理一个灰度图像，它有很多盐和胡椒的噪音，并逐渐认识到MedianBlur是非常有用的。所以我使用了(cv2.medianBlur())。它起了作用，但没有达到我想要的效果。因此，我正在寻找它所使用的实际算法，并得到了以下解释：如果内核大小(K)为5，那么对于每5个(行计数)x5(列计数)平方窗口，该窗口的中心...

问Opencv MedianBlur实际上是如何工作的？
EN

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问Opencv MedianBlur实际上是如何工作的？EN