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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.
Sometimes, you find a drawing or similar image useful for a Wikipedia article, that was saved as a JPEG but should have been saved as a PNG.JPEG is good for images where the color changes fluidly throughout the image, like in a photograph, whereas PNG files are good for images with relatively few colors, such as a drawing of a flag, a chart, or a map; note that sometimes SVG is better.
This image has partial transparency (254 possible levels of transparency between fully transparent and fully opaque). It can be transparent against any background despite being anti-aliased. Some image formats, such as PNG and TIFF, also allow partial transparency through an alpha channel, which solves the edge limitation problem.
The background image is used as the bottom layer, and the image with parts to be added are placed in a layer above that. Using an image layer mask , all but the parts to be merged is hidden from the layer, giving the impression that these parts have been added to the background layer.
For image processing, deconvolution is the process of approximately inverting the process that caused an image to be blurred. Specifically, unsharp masking is a simple linear image operation—a convolution by a kernel that is the Dirac delta minus a gaussian blur kernel.
An example of an image blurred using a box blur. A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter.