smooth_imagesmooth_imageSmoothImageSmoothImagesmooth_image (Operator)

Name

smooth_imagesmooth_imageSmoothImageSmoothImagesmooth_image — Smooth an image using various filters.

Signature

Description

smooth_imagesmooth_imageSmoothImageSmoothImagesmooth_image smooths gray images using recursive filters originally developed by Deriche and Shen and using the non-recursive Gaussian filter. The following filters can be chosen via the parameter FilterFilterFilterfilterfilter: 'deriche1', 'deriche2', 'shen' and 'gauss'. The “filter width” (i.e., the range of the filter and thereby result of the filter) can be of any size. In the case that the Deriche or Shen is chosen it decreases by increasing the filter parameter AlphaAlphaAlphaalphaalpha and increases in the case of the Gauss filter (and AlphaAlphaAlphaalphaalpha corresponds to the standard deviation of the Gaussian function). An approximation of the appropriate size of the filter width AlphaAlphaAlphaalphaalpha is performed by the operator info_smoothinfo_smoothInfoSmoothInfoSmoothinfo_smooth.

Non-recursive filters like the Gaussian filter are often implemented using filter-masks. In this case the runtime of the operator increases with increasing size of the filter mask. The runtime of the recursive filters remains constant; except the border treatment becomes a little bit more time consuming. The Gaussian filter becomes slow in comparison to the recursive ones but is in contrast to them isotropic (the filter 'deriche2' is only weakly direction sensitive). A comparable result of the smoothing is achieved by choosing the following values for the parameter: Alpha(deriche2) = Alpha(deriche1) / 2, Alpha(shen) = Alpha(deriche1) / 2, Alpha(gauss) = 1.77 / Alpha(deriche1).

For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.

Attention

Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.

Execution Information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Automatically parallelized on tuple level.
• Automatically parallelized on channel level.
• Automatically parallelized on internal data level.

Parameters

info_smooth('deriche2',0.5,Size,Coeffs)
smooth_image(Input,Smooth,'deriche2',7)

info_smooth('deriche2',0.5,Size,Coeffs);
smooth_image(Input,&Smooth,'deriche2',7);

info_smooth('deriche2',0.5,Size,Coeffs)
smooth_image(Input,Smooth,'deriche2',7)

info_smooth('deriche2',0.5,Size,Coeffs)
smooth_image(Input,Smooth,'deriche2',7)

Result

If the parameter values are correct the operator smooth_imagesmooth_imageSmoothImageSmoothImagesmooth_image returns the value 2 ( H_MSG_TRUE) . The behavior in case of empty input (no input images available) is set via the operator set_system('no_object_result',<Result>)set_system("no_object_result",<Result>)SetSystem("no_object_result",<Result>)SetSystem("no_object_result",<Result>)set_system("no_object_result",<Result>). If necessary an exception is raised.

Possible Predecessors

read_imageread_imageReadImageReadImageread_image

Possible Successors

thresholdthresholdThresholdThresholdthreshold, dyn_thresholddyn_thresholdDynThresholdDynThresholddyn_threshold, regiongrowingregiongrowingRegiongrowingRegiongrowingregiongrowing

Alternatives

binomial_filterbinomial_filterBinomialFilterBinomialFilterbinomial_filter, gauss_filtergauss_filterGaussFilterGaussFiltergauss_filter, mean_imagemean_imageMeanImageMeanImagemean_image, derivate_gaussderivate_gaussDerivateGaussDerivateGaussderivate_gauss, isotropic_diffusionisotropic_diffusionIsotropicDiffusionIsotropicDiffusionisotropic_diffusion

See also

info_smoothinfo_smoothInfoSmoothInfoSmoothinfo_smooth, median_imagemedian_imageMedianImageMedianImagemedian_image, sigma_imagesigma_imageSigmaImageSigmaImagesigma_image, anisotropic_diffusionanisotropic_diffusionAnisotropicDiffusionAnisotropicDiffusionanisotropic_diffusion

References

R.Deriche: “Fast Algorithms for Low-Level Vision”; IEEE Transactions on Pattern Analysis and Machine Intelligence; PAMI-12, no. 1; S. 78-87; 1990.

Module

Foundation