Blur NodeThe Blur node enables you to blur an image, or selected channels of an image. The Blur node is a convolution filter that performs the equivalent of low-pass filtering in the frequency domain, where the high frequencies from a signal are filtered out and only the low frequencies are allowed to pass through. Because areas of greatest visual detail in an image are expressed as high frequencies, the low-pass filter effectively blurs the image. To use the Blur node, specify a value in the Blur parameter tab. If necessary, you can customize the kernel used in the convolution by modifying the parameters on the Filtering tab. Blur Parameter Tab![]() BlurThe Blur parameter enables you to specify the number of adjacent pixels whose values will be used to modify the current pixel. This value is expressed as a percentage of the total number of pixels in the image. You set this parameter by keying a value in the range of 0 to 1 into the data entry field or by using the mouse to set the tuner to the desired value. A value of 0 would leave the image unmodified by the convolution. A value of 1 would specify a kernel the size of the image, so that every pixel in the image would contribute to the modification of the current pixel. In most cases you will select values within a narrow range of the low end of the scale. Channel MaskThe Channel Mask parameter enables you to inhibit the modification of any channel to the blur effect by deselecting the Red, Green, Blue, Alpha, or Other channel icons. Control Image ParametersThe Control Action and Control Channel menus enable you to specify how an optional control image input will govern the node operation, as explained in "Using Control Images with Filter Nodes ." Filtering Parameter TabThe convolution used for the blur modifies each pixel in the image, in turn, based on the values of surrounding pixels. The matrix that delineates this pixel area is known as a kernel. The Filtering tab parameters enable you to define the kernel attributes to control the blur. ![]() Kernel TypeThe Kernel Type parameter features a popup menu that allows you to specify Integer or Floating Point math for the calculation.
Kernel ShapeThe Kernel Shape parameter enables you to determine the extent to which each pixel in the kernel will contribute to the convolution by choosing a function that describes the distribution curve used to weight the sample. The following functions are available in the Kernel Shape popup menu: Constant, Linear, Quadratic, Cubic, or Gaussian. The Constant function, for example, is equivalent to a box filter and applies the same weight to every cell (pixel) value in the kernel when it is factored. The weighting distribution becomes more complex as you select the other functions: Linear is equivalent to a triangle filter, and so on, with Gaussian being the most complex and computationally intensive. The optimal choice will depend on the nature of the source image, the effect you wish to achieve, and how much time you are willing to devote to processing. Border PixelsBy the nature of the convolve operation used to blur the image, the pixel currently being modified is in the center of the cell matrix that composes the kernel. Therefore, when convolving a pixel that borders the image (or when the kernel is large), there will not be adjacent pixels on all sides to contribute values to the convolution. The Border Pixels parameter enables you to assign pixel values to these "empty" kernel cells by selecting one of the following options from a popup menu:
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