Howtosignalprocessing
- How do I choose kernel size for Gaussian filter?
- Which of the kernel can be used for smoothing of the images?
- What is a good kernel size for Gaussian blur?
- What is Gaussian smoothing in image processing?
How do I choose kernel size for Gaussian filter?
For example if sigma = 1 then the gaussian is greater than epsilon = 0.01 when x <= 2.715 so a filter radius = 3 (width = 2*3 + 1 = 7) is sufficient. If you reduce/increase epsilon then you will need a larger/smaller radius.
Which of the kernel can be used for smoothing of the images?
In the case of smoothing, the filter is the Gaussian kernel. Therefore, if we are expecting signal in our images that is of Gaussian shape, and of FWHM of say 10mm, then this signal will best be detected after we have smoothed our images with a 10mm FWHM Gaussian filter.
What is a good kernel size for Gaussian blur?
Typically, however, it's uncommon to use a kernel size larger than around 50 or so as things are usually already pretty blurry by that point. The Gaussian blur is a great example of simple mathematics put to a powerful use in image processing.
What is Gaussian smoothing in image processing?
The Gaussian smoothing operator is a 2-D convolution operator that is used to `blur' images and remove detail and noise. In this sense it is similar to the mean filter, but it uses a different kernel that represents the shape of a Gaussian (`bell-shaped') hump.
