Applies the bilateral filter to an image

result = cv.bilateralFilter(img)
result = cv.bilateralFilter(img, 'OptionName', optionValue, ...)

Input

• img Source 8-bit or floating-point, 1-channel or 3-channel image.

Output

• result Destination image of the same size and type as src.

Options

• Diameter Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from SigmaSpace. Default: 7
• SigmaColor Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see SigmaSpace) will be mixed together, resulting in larger areas of semi-equal color. Default: 50.0
• SigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see SigmaColor). When Diameter>0, it specifies the neighborhood size regardless of SigmaSpace. Otherwise, Diameter is proportional to SigmaSpace. Default: 50.0
• BorderType border mode used to extrapolate pixels outside of the image. See cv.copyMakeBorder. Default: 'Default'

The function applies bilateral filtering to the input image, as described in [CVonline]. cv.bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters.

Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish".

Filter size: Large filters (Diameter > 5) are very slow, so it is recommended to use Diameter=5 for real-time applications, and perhaps Diameter=9 for offline applications that need heavy noise filtering.

References

[CVonline]:

http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html