What is Blurring?
As you can see the name Blurring, it blurs an image by:
- Convolution : It uses a convolution layer in order to get blurred images. It can be used as:
cv2.filter2D(src, ddepth, kernel) where:- src = input image
- ddepth = desired depth of the destination image.
- kernel = convolution kernel (or rather a correlation kernel), a single-channel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually.
- Regular : Averaging done by convolving the image with a normalized box filter. This takes the pixels under the box and replaces the central element. Box size needs to odd and positive It can be used as: cv2.blur(src, ksize) where:
- src = input image; it can have any number of channels, which are processed independently.
- ksize = blurring kernel size.
- Gaussian : instead of box filter, use gaussian kernel. It can be used as:
cv2.GaussianBlur(src, ksize, sigmaX, sigmaY) where:- src = input image; it can have any number of channels, which are processed independently.
- ksize = blurring kernel size.
- sigmaX = Gaussian kernel standard deviation in X direction.
- sigmaY = Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height.
- Median : Takes median of all the pixels under kernel area and central element is replaced with this median value. It can be used as:
cv2.medianBlur(src, st, ksize) where:- src = input image; it can have any number of channels, which are processed independently.
- dst = output image of the same size and the same number of channels as src.
- ksize = blurring kernel size.
- Bilateral Filter : bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. It can be used as:
cv2.bilateralFilter(src, d, sigmaColor, sigmaSpace) where:- src = Source 8-bit or floating-point, 1-channel or 3-channel image.
- d = Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.
- 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.
- 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 d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace.