Smoothing

List of Operators ↓

This chapter contains operators for smoothing filters. Further information about filtering can be found at the introduction to the chapter Filters.

General information about smoothing filters

Smoothing operators are filters that help to suppress noise in an image. For this purpose it is assumed, that in the undisturbed or true image the gray value of a given data point does not completely differ from its surroundings, ideally even varies only little. Thus, to suppress noise, it can be useful to replace the measured gray value with an estimate based on surrounding data points. Such an estimate can be done in different ways, so HALCON provides different smoothing operators.

The operators differ in speed and suitability for different kinds of noise. Information like the complexity (runtime dependence on the image size) is, if available, given in the operator reference. While most operators treat a single image, some can process depending images (e.g., multichannel filters like mean_nmean_nMeanNMeanNMeanNmean_n and rank_nrank_nRankNRankNRankNrank_n, or edge-preserving filters like guided_filterguided_filterGuidedFilterGuidedFilterGuidedFilterguided_filter and bilateral_filterbilateral_filterBilateralFilterBilateralFilterBilateralFilterbilateral_filter, which additionally use guidance images). Please note that some filters have both possibilities and more information is given in the specific operator reference.

Smoothing filters for single images with random noise

These smoothing filters apply their smoothing function on each channel of the input image separately and return a smoothed image with the same number of channels. In the following table we list implemented variants of smoothing filters for a single image with random noise and apply them for three different variants of random noise. The images in the table shall give an idea of the operators capability, but please note that the smoothed images highly depend on the input parameters and the individual image for every operator. For comparison, the different noisy images without filtering are given in the first row of the table. The undisturbed image without noise is shown in the following figure ((1) the full image as well as (2) its part by means of which possible effects on edges and remains from Salt & Pepper noise are visualized more clearly).

We marked filters recommended due to their special suitability concerning speed (S), edge-preservation (E), or a compromise between these two (C). The numbers in square brackets refer to further information that is given in a list below the table.

Further information related to the numbers in square brackets used in the table above:

1. The numbers in the column 'Time' are indications about the time the operator uses to process an image. The numbers are obtained from averaging over multiple runs on the three different noise images and given in arbitrary units. Note also that the runtime of an operator depends on many factors, not at least on the used parameters and the image size. For each filter, the first number is for an image of size 800x600, the second for an image of size 1497x1160. The shown images are parts of the smaller image with parameters reasonable to us (we do not claim to have found the parameters resulting neither necessarily in the best image smoothing nor the smallest runtime). Even if two filters have the same parameter name as input, we did not necessarily use the same parameter value for both of them. Therefore, these numbers are to be understood as an indicator only.

2. This operator can be used iteratively.

3. This operator uses a guidance image (which can be the ImageImageImageImageimageimage itself).

Smoothing filters for single images with systematic noise

Video images composed of two half images can have systematic errors. In such a case, the operator fill_interlacefill_interlaceFillInterlaceFillInterlaceFillInterlacefill_interlace can help.

Operators designed for smoothing over multiple channels

These smoothing filters take an image with multiple channels as input and return a single channel (gray value) image. In HALCON, the following filters of this group are implemented:

• mean_nmean_nMeanNMeanNMeanNmean_n

• rank_nrank_nRankNRankNRankNrank_n

Further operators

In addition to the smoothing filters, this chapter contains the following operator: info_smoothinfo_smoothInfoSmoothInfoSmoothInfoSmoothinfo_smooth, which returns information related to the different filters used by the operator smooth_imagesmooth_imageSmoothImageSmoothImageSmoothImagesmooth_image.

Glossary

In the following, the most important terms that are used in the context of smoothing filters are described:

smoothing

Smoothing means to apply a filter function on the given data to capture the main data patterns while removing noise.

random noise

Random noise is a stationary variation of brightness or color information by a small random amount for every pixel with an assumed mean of 0 over the total image.

systematic noise

Systematic noise is predictable noise, caused, e.g., by the specific setup used to acquire the images.

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List of Operators

anisotropic_diffusionAnisotropicDiffusionanisotropic_diffusionAnisotropicDiffusionanisotropic_diffusion

Perform an anisotropic diffusion of an image.

bilateral_filterBilateralFilterbilateral_filterBilateralFilterbilateral_filter

bilateral filtering of an image.

binomial_filterBinomialFilterbinomial_filterBinomialFilterbinomial_filter

Smooth an image using the binomial filter.

eliminate_min_maxEliminateMinMaxeliminate_min_maxEliminateMinMaxeliminate_min_max

Smooth an image in the spatial domain to suppress noise.

eliminate_spEliminateSpeliminate_spEliminateSpeliminate_sp

Replace values outside of thresholds with average value.

fill_interlaceFillInterlacefill_interlaceFillInterlacefill_interlace

Interpolate 2 video half images.

gauss_filterGaussFiltergauss_filterGaussFiltergauss_filter

Smooth using discrete Gauss functions.

guided_filterGuidedFilterguided_filterGuidedFilterguided_filter

Guided filtering of an image.

info_smoothInfoSmoothinfo_smoothInfoSmoothinfo_smooth

Information on smoothing filter smooth_imagesmooth_imageSmoothImageSmoothImageSmoothImagesmooth_image.

isotropic_diffusionIsotropicDiffusionisotropic_diffusionIsotropicDiffusionisotropic_diffusion

Perform an isotropic diffusion of an image.

mean_imageMeanImagemean_imageMeanImagemean_image

Smooth by averaging.

mean_image_shapeMeanImageShapemean_image_shapeMeanImageShapemean_image_shape

Smooth image using a mean filter with arbitrary mask.

mean_nMeanNmean_nMeanNmean_n

Average gray values over several channels.

mean_spMeanSpmean_spMeanSpmean_sp

Suppress salt and pepper noise.

median_imageMedianImagemedian_imageMedianImagemedian_image

Compute a median filter with various masks.

median_rectMedianRectmedian_rectMedianRectmedian_rect

Compute a median filter with rectangular masks.

median_separateMedianSeparatemedian_separateMedianSeparatemedian_separate

Separated median filtering with rectangle masks.

median_weightedMedianWeightedmedian_weightedMedianWeightedmedian_weighted

Weighted median filtering with different rank masks.

midrange_imageMidrangeImagemidrange_imageMidrangeImagemidrange_image

Calculate the average of maximum and minimum inside any mask.

rank_imageRankImagerank_imageRankImagerank_image

Compute a rank filter with arbitrary masks.

rank_nRankNrank_nRankNrank_n

Return gray values with given rank from multiple channels.

rank_rectRankRectrank_rectRankRectrank_rect

Compute a rank filter with rectangular masks.

sigma_imageSigmaImagesigma_imageSigmaImagesigma_image

Non-linear smoothing with the sigma filter.

smooth_imageSmoothImagesmooth_imageSmoothImagesmooth_image

Smooth an image using various filters.

trimmed_meanTrimmedMeantrimmed_meanTrimmedMeantrimmed_mean

Smooth an image with an arbitrary rank mask.

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