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The difference between a small and large Gaussian blur. In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce image noise and reduce detail.
Consequently, the foreground and background are often blurred, with the blur increasing with distance above or below the center of the image. In a photograph of a full-size scene, the DoF is considerably greater; in some cases, it is difficult to have much of the scene outside the DoF, even at the lens's maximum aperture. Thus a difference in ...
Finally, compare the image with the original. Make sure there are no holes in the background layer (if you have one). If the new version is too sharp, you can utilize the blur filter or tool to fix this, or use Colors > Levels to mess with the Alpha channel. Once it looks correct, save the image as a PNG.
From left to right: an original photo with no bokeh or blur; the same photo with synthetic bokeh effect applied to its background; the same photo with Gaussian blur applied to its background Bokeh can be simulated by convolving the image with a kernel that corresponds to the image of an out-of-focus point source taken with a real camera.
In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more.This is accomplished by doing a convolution between the kernel and an image.
For calculating the image containing only the background, a series of preceding images are averaged. For calculating the background image at the instant t: (,,) = = (,,) where N is the number of preceding images taken for averaging. This averaging refers to averaging corresponding pixels in the given images.
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