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| [[File:Gaussian blur example.jpg|thumb|250px|The effects of a small and a large Gaussian blur]]
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| A '''Gaussian blur''' (also known as Gaussian smoothing) is the result of blurring an image by a [[Gaussian function]]. It is a widely used effect in graphics software, typically to reduce [[image noise]] and reduce detail. The visual effect of this blurring technique is a smooth blur resembling that of viewing the [[image]] through a translucent screen, distinctly different from the [[bokeh]] effect produced by an out-of-focus lens or the shadow of an object under usual illumination. Gaussian smoothing is also used as a pre-processing stage in [[computer vision]] algorithms in order to enhance image structures at different scales—see [[scale space representation]] and [[scale space implementation]].
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| Mathematically, applying a Gaussian blur to an image is the same as [[convolution|convolving]] the image with a [[Gaussian function]]. This is also known as a two-dimensional [[Weierstrass transform]]. By contrast, convolving by a circle (i.e., a circular [[box blur]]) would more accurately reproduce the [[bokeh]] effect. Since the [[Fourier transform]] of a Gaussian is another Gaussian, applying a Gaussian blur has the effect of reducing the image's high-frequency components; a Gaussian blur is thus a [[low pass filter]].
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| ==Mechanics==
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| [[File:Halftone, Gaussian Blur.jpg|thumb|right|Gaussian blur can be used in order to obtain a smooth grayscale digital image of a [[halftone]] print]]
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| The Gaussian blur is a type of image-blurring filter that uses a Gaussian function (which also expresses the [[normal distribution]] in statistics) for calculating the [[transformation (mathematics)|transformation]] to apply to each pixel in the image. The equation of a Gaussian function in one dimension is
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| :<math>G(x) = \frac{1}{\sqrt{2\pi \sigma^2}} e^{-\frac{x^2}{2 \sigma^2}}</math>
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| in two dimensions, it is the product of two such Gaussians, one in each dimension:
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| :<math>G(x,y) = \frac{1}{2\pi \sigma^2} e^{-\frac{x^2 + y^2}{2 \sigma^2}}</math><ref name="ShapiroStockman">Shapiro, L. G. & Stockman, G. C: "Computer Vision", page 137, 150. Prentice Hall, 2001</ref><ref name="NixonAguado">Mark S. Nixon and Alberto S. Aguado. ''Feature Extraction and Image Processing''. Academic Press, 2008, p. 88.</ref>
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| where ''x'' is the distance from the origin in the horizontal axis, ''y'' is the distance from the origin in the vertical axis, and ''σ'' is the [[standard deviation]] of the Gaussian distribution. When applied in two dimensions, this formula produces a surface whose [[:wikt:contour|contour]]s are [[concentric circles]] with a Gaussian distribution from the center point. Values from this distribution are used to build a [[convolution]] matrix which is applied to the original image. Each pixel's new value is set to a [[weighted average]] of that pixel's neighborhood. The original pixel's value receives the heaviest weight (having the highest Gaussian value) and neighboring pixels receive smaller weights as their distance to the original pixel increases. This results in a blur that preserves boundaries and edges better than other, more uniform blurring filters; see also [[scale space implementation]].
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| In theory, the Gaussian function at every point on the image will be non-zero, meaning that the entire image would need to be included in the calculations for each pixel. In practice, when computing a discrete approximation of the Gaussian function, pixels at a distance of more than 3''σ'' are small enough to be considered effectively zero. Thus contributions from pixels outside that range can be ignored. Typically, an image processing program need only calculate a matrix with dimensions <math>\lceil6\sigma\rceil</math> × <math>\lceil6\sigma\rceil</math> (where <math>\lceil \cdot \rceil</math> is the [[Floor and ceiling functions|ceiling function]]) to ensure a result sufficiently close to that obtained by the entire gaussian distribution.
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| In addition to being circularly symmetric, the Gaussian blur can be applied to a two-dimensional image as two independent one-dimensional calculations, and so is termed [[separable filter]]. That is, the effect of applying the two-dimensional matrix can also be achieved by applying a series of single-dimensional Gaussian matrices in the horizontal direction, then repeating the process in the vertical direction. In computational terms, this is a useful property, since the calculation can be performed in <math>O\left(w_\text{kernel} w_\text{image} h_\text{image}\right) + O\left(h_\text{kernel} w_\text{image} h_\text{image}\right)</math> time (where ''h'' is height and ''w'' is width; see [[Big O notation]]), as opposed to <math>O\left(w_\text{kernel} h_\text{kernel} w_\text{image} h_\text{image}\right)</math> for a non-separable kernel.
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| Applying multiple, successive gaussian blurs to an image has the same effect as applying a single, larger gaussian blur, whose radius is the square root of the sum of the squares of the blur radii that were actually applied. For example, applying successive gaussian blurs with radii of 6 and 8 gives the same results as applying a single gaussian blur of radius 10, since <math>\sqrt{6^2 + 8^2} = 10</math>. Because of this relationship, processing time cannot be saved by simulating a gaussian blur with successive, smaller blurs — the time required will be at least as great as performing the single large blur.
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| [[Image:Gaussian blur before downscaling.png|thumb|128x128px|Two downscaled images of the [[Flag of the Commonwealth of Nations]]. Before downscaling, a Gaussian blur was applied to the bottom image but not to the top image. The blur makes the image less sharp, but prevents the formation of [[Moiré pattern]] aliasing artifacts.]]
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| Gaussian blurring is commonly used when reducing the size of an image. When [[downsampling]] an image, it is common to apply a low-pass filter to the image prior to resampling. This is to ensure that spurious high-frequency information does not appear in the downsampled image ([[aliasing]]). Gaussian blurs have nice properties, such as having no sharp edges, and thus do not introduce ringing into the filtered image.
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| == Low-pass filter ==
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| {{Expand section|date=March 2009}}
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| Gaussian blur is a [[low-pass filter]], attenuating high frequency signals.
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| Its amplitude [[Bode plot]] (the [[log scale]] in the [[frequency domain]]) is a [[parabola]].
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| ==Sample Gaussian matrix==
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| This is a sample matrix, produced by sampling the Gaussian filter kernel (with σ = 0.84089642) at the midpoints of each pixel and then normalizing. Note that the center element (at [0, 0]) has the largest value, decreasing symmetrically as distance from the center increases.
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| <!-- This is contradictory, above it says ceil(6*sigma) dimension size, which is 6, here we use 7 -->
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| {| cellpadding="2" style="text-align:center;width:100%;max-width:49em;"
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| |0.00000067 || 0.00002292 || '''0.00019117''' || 0.00038771 || '''0.00019117 '''|| 0.00002292 || 0.00000067
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| |0.00002292 || 0.00078634 || 0.00655965 || 0.01330373 || 0.00655965 || 0.00078633 || 0.00002292
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| |'''0.00019117''' || 0.00655965 || 0.05472157 || 0.11098164 || 0.05472157 || 0.00655965 || '''0.00019117'''
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| |0.00038771 || 0.01330373 || 0.11098164 || '''0.22508352''' || 0.11098164 || 0.01330373 || 0.00038771
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| |'''0.00019117''' || 0.00655965 || 0.05472157 || 0.11098164 || 0.05472157 || 0.00655965 || '''0.00019117'''
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| |0.00002292 || 0.00078633 || 0.00655965 || 0.01330373 || 0.00655965 || 0.00078633 || 0.00002292
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| |-
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| |0.00000067 || 0.00002292 || '''0.00019117''' || 0.00038771 || '''0.00019117 '''|| 0.00002292 || 0.00000067
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| |}
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| Note that 0.22508352 (the central one) is 1177 times larger than 0.00019117 which is just outside 3σ.
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| ==Implementation==
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| A Gaussian blur effect is typically generated by convolving an image with a kernel of Gaussian values. In practice, it is best to take advantage of the Gaussian blur’s separable property by dividing the process into two passes. In the first pass, a one-dimensional kernel is used to blur the image in only the horizontal or vertical direction. In the second pass, another one-dimensional kernel is used to blur in the remaining direction. The resulting effect is the same as convolving with a two-dimensional kernel in a single pass, but requires fewer calculations.
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| Discretisation is typically achieved by sampling the Gaussian filter kernel at discrete points, normally at positions corresponding to the midpoints of each pixel. This reduces the computational cost but, for very small filter kernels, point sampling the Gaussian function with very few samples leads to a large error. In these cases, accuracy is maintained (at a slight computational cost) by integration of the Gaussian function over each pixel's area.<ref name="Reinhard">Erik Reinhard. ''High dynamic range imaging: Acquisition, Display, and Image-Based Lighting''. Morgan Kaufmann, 2006, pp. 233–234.</ref>
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| When converting the Gaussian’s continuous values into the discrete values needed for a kernel, the sum of the values will be different from 1. This will cause a darkening or brightening of the image. To remedy this, the values can be normalized by dividing each term in the kernel by the sum of all terms in the kernel.
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| ==Common uses==
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| [[File:Edge Image.gif|thumb|right|This shows how smoothing affects edge detection. With more smoothing, fewer edges are detected]]
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| Gaussian smoothing is commonly used with [[edge detection]]. Most edge-detection algorithms are sensitive to noise; the 2-D Laplacian filter, built from a discretization of the [[Laplace operator]], is highly sensitive to noisy environments. Using a Gaussian Blur filter before edge detection aims to reduce the level of noise in the image, which improves the result of the following edge-detection algorithm. This approach is commonly referred to as [[Laplacian of Gaussian]], or LoG filtering.<ref>{{cite web|url=http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm|title=Spatial Filters - Laplacian of Gaussian|last=Fisher, Perkins, Walker & Wolfart|year=2003|accessdate =2010-09-13}}</ref>
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| ==See also==
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| *[[Image noise]]
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| *[[Gaussian filter]]
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| *[[Gaussian pyramid]]
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| *[[Infinite impulse response]] (IIR)
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| *[[Scale space implementation]]
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| *[[Median filter]]
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| ==Notes and references==
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| {{reflist}}
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| == External links ==
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| *[http://www.gamerendering.com/2008/10/11/gaussian-blur-filter-shader/ GLSL implementation of a separable gaussian blur filter].
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| *Example for [http://holiday.snrk.de/SnarkSearch.cgi Gaussian blur (low pass filtering) applied to a wood-block print and an etching] in order to remove details for picture comparison.
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| *Mathematica [http://reference.wolfram.com/mathematica/ref/GaussianFilter.html GaussianFilter] function
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| {{DEFAULTSORT:Gaussian Blur}}
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| [[Category:Image processing]]
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| [[Category:Gaussian function]]
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| [[Category:Image noise reduction techniques]]
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