blocks|key|4373890|text|据我所知，中值滤波是降低噪声的最佳解决方案。我建议使用3x3窗口的中值滤波器。请参见函数cv::medianBlur()。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4373891|但是，当同时使用任何噪声过滤和OCR时要小心。它会导致识别准确率的下降。|4373892|另外，我建议尝试使用两个函数(cv::erode()和cv::dilate())。但我不确定它会不会比使用Windows3x3的cv::medianBlur()解决方案更好。|4373893|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.opencv.org/3.1.0/d4/d86/group__imgproc__filter.html#ga564869aa33e58769b4469101aac458f9^0|18|G|0|0|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|O]]]]

As I know the median filter is the best solution to reduce noise. I would recommend to use median filter with 3x3 window. See function <a href="http://docs.opencv.org/3.1.0/d4/d86/group__imgproc__filter.html#ga564869aa33e58769b4469101aac458f9" rel="nofollow noreferrer">cv::medianBlur()</a>. 

But be careful when use any noise filtration simultaneously with OCR. Its can lead to decreasing of recognition accuracy.

Also I would recommend to try using pair of functions (cv::erode() and cv::dilate()). But I'm not shure that it will best solution then cv::medianBlur() with window 3x3.

blocks|key|4373942|text|尝试像这样对图像进行阈值处理。确保你的src是灰度的。这种方法将只保留强度在150和255之间的像素。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4373943|threshold(src,+output,+150,+255,+CV_THRESH_BINARY+%7C+CV_THRESH_OTSU);|code-block|syntax|javascript|4373944|您可能想要反转图像，因为您正在尝试对灰色像素求反。操作完成后，再次反转以获得您想要的结果。|4373945|entityMap^0|J|3|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|S|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|N|$]]

Try thresholding the image like this. Make sure your <code>src</code> is in grayscale. This method will only retain the pixels which are between 150 and 255 intensity. 

<pre><code>threshold(src, output, 150, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
</code></pre>

You might want to invert the image as you are trying to negate the gray pixels. After the operation, invert it again to get your desired result.

blocks|key|4373975|text|如果您非常担心删除像素，这可能会损害您的OCR检测。在不添加人工制品的情况下，尽可能地保持原始图像的纯净。然后，您应该创建一个blob过滤器。并删除小于n个像素左右的任何斑点。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4373976|我不打算写代码，但我知道这很好用，因为我自己也使用它，尽管我不使用openCV+(出于速度的原因，我编写了自己的多线程blobfilter+)。很抱歉，我不能在这里分享我的代码。只是描述如何做到这一点。|4373977|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

If you are very worried of removing pixels that could hurt your OCR detection. Without adding artefacts ea be as pure to the original as possible. Then you should create a blob filter. And delete any blobs that are smaller then n pixels or so. 

Not going to write code, but i know this works great as i use this myself, though i dont use openCV (i wrote my own multithreaded blobfilter out of speed reasons). And sorry but i cannot share my code here. Just describing how to do it.

blocks|key|4374440|text|如果处理时间不是问题，在这种情况下一个非常有效的方法是计算所有黑色连接分量，并删除那些小于几个像素的分量。它将删除所有噪点(除了那些接触有效组件的点)，但保留所有字符和文档结构(线条等)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4374441|要使用的函数将是connectedComponentWithStats+(在您可能需要生成负片图像之前，带有THRESH_BINARY_INV的threshold函数在本例中将起作用)，在找到小的连通组件的位置绘制白色矩形。|offset|length|style|CODE|4374442|事实上，这种方法可以用来查找字符，定义为给定最小和最大大小的连通分量，以及给定范围内的纵横比。|4374443|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.opencv.org/3.0-beta/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#connectedcomponents|1|http://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html?#threshold^0|0|1I|H|8|R|0|20|9|1|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@$D|V|E|W|F|G]]|9|@$D|X|E|Y|1|Z]|$D|10|E|11|1|12]]|A|$]]|$1|H|3|I|5|6|7|13|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]|R|$5|M|N|O|A|$P|S]]]]

If processing time is not an issue, a very effective method in this case would be to compute all black connected components, and remove those smaller than a few pixels. It would remove all the noisy dots (apart those touching a valid component), but preserve all characters and the document structure (lines and so on).

The function to use would be <a href="http://docs.opencv.org/3.0-beta/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#connectedcomponents" rel="nofollow noreferrer">connectedComponentWithStats</a> (before you probably need to produce the negative image, the <a href="http://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html?#threshold" rel="nofollow noreferrer">threshold</a> function with <code>THRESH_BINARY_INV</code> would work in this case), drawing white rectangles where small connected components where found.

In fact, this method could be used to find characters, defined as connected components of a given minimum and maximum size, and with aspect ratio in a given range.

I have an image here with a table.. In the column on the right the background is filled with noise

How to detect the areas with noise? I only want to apply some kind of filter on the parts with noise because I need to do OCR on it and any kind of filter will reduce the overall recognition

And what kind of filter is the best to remove the background noise in the image?

As said I need to do OCR on the image

<a href="https://i.stack.imgur.com/aTONl.png" rel="noreferrer"><img src="https://i.stack.imgur.com/aTONl.png" alt="enter image description here"></a>

OpenCV - Removal of noise in image

我有一张照片，上面有一张桌子..在右边的列中，背景充满了杂音如何检测有噪声的区域？我只想对有噪声的部分应用某种滤镜，因为我需要对其进行OCR，而任何一种滤镜都会降低整体识别率哪种滤波器对去除图像中的背景噪声效果最好？如上所述，我需要在图像上做OCR​​

问OpenCV -去除图像中的噪声
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