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is available, and averaging is then realized in a local neighbourhood. Results are acceptable if the noise is smaller in size than the smallest objects of interest in the image, but blurring of edges is a serious disadvantage. In the case of smoothing within a single image, one has to assume that
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there are no changes in the gray levels of the underlying image data. This assumption is clearly violated at locations of image edges, and edge blurring is a direct consequence of violating the assumption.
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549:{\displaystyle M={\frac {1}{16}}{\begin{bmatrix}1&2&1\\2&4&2\\1&2&1\\\end{bmatrix}}}
452:{\displaystyle M={\frac {1}{10}}{\begin{bmatrix}1&1&1\\1&2&1\\1&1&1\\\end{bmatrix}}}
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https://web.archive.org/web/20061019072001/http://www.gamedev.net/reference/programming/features/imageproc/
348:{\displaystyle M={\frac {1}{9}}{\begin{bmatrix}1&1&1\\1&1&1\\1&1&1\\\end{bmatrix}}}
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The significance of the central pixel may be increased, as it approximates the properties of noise with a
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Averaging is a special case of discrete convolution. For a 3 by 3 neighbourhood, the convolution mask
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A suitable page for beginners about matrices is at:
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may be too technical for most readers to understand
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590:This article related to radio communications is a
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361:probability distribution:
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596:expanding it
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192:October 2009
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79:Please help
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249:Description
137:August 2014
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36:improve it
42:talk page
359:Gaussian
232:Within
178:Please
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