Blurring and Local Blurring

Blurring and Local Blurring: 2007/06/12

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OutlineIntroductionBlurringDiscrete GaussianBinomial BlurringRecursive Gaussian IIRLocal Blurring**

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IntroductionThis section describes several methods that can be applied to reduce noise on images.BlurringBlurring is the traditional approach for removing noise from images.The effect of blurring on the image spectrum is to attenuate high spatial frequencies.

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OutlineIntroductionBlurringDiscrete GaussianBinomial BlurringRecursive Gaussian IIRLocal Blurring**

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Header:#include "itkDiscreteGaussianImageFilter.h"filter->SetVariance( gaussianVariance );filter->SetMaximumKernelWidth( maxKernelWidth );

**Discrete Gaussian

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**Discrete GaussianEffect of the DiscreteGaussianImageFilter on a slice from a MRI proton density image of the brain.

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**Variance=3 maxKernelWidth=3*3Discrete GaussianVariance=7maxKernelWidth=3*3

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**Discrete GaussianVariance=3 maxKernelWidth=5*5Variance=3 maxKernelWidth=7*7

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Binomial BlurringBinomial Blurring computes a nearest neighbor average along each dimensionThe process is repeated a number of times, as specified by the user.**

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Binomial filters. 1D versions**Binomial Blurring

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In principle, after a large number of iterations the result will approach the convolution with a Gaussian.Header:#include "itkBinomialBlurImageFilter.h"

**Binomial Blurring

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**Binomial BlurringRepetitions=2Repetitions=8

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Finite Impulse Response (FIR)Infinite Impulse Response (IIR)**Recursive Gaussian IIR(FIR)(IIR)

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Recursive Gaussian IIRHeader:#include "itkRecursiveGaussianImageFilter.h

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**Recursive Gaussian IIRSigma=3Sigma=5Sigma=7

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OutlineIntroductionBlurringDiscrete GaussianBinomial BlurringRecursive Gaussian IIRLocal Blurring**

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Local BlurringIn some cases it is desirable to compute smoothing in restricted regions of the image, or to do it using different parameters that are computed locally.

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ItkSoftwareGuidehttp://www.engr.udayton.edu/faculty/jloomis/ece563/notes/filter/smooth/smooth.htmlhttp://ssip2003.info.uvt.ro/lectures/gui/smoothing_techniques_in_image_processing.ppthttp://www.cee.hw.ac.uk/hipr/html/gsmooth.html**

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The filter requires the user to provide a value for the variance associated with the Gaussian kernel.

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