ClassesClasses | | Operators

wiener_filterwiener_filterWienerFilterWienerFilter (Operator)

Name

wiener_filterwiener_filterWienerFilterWienerFilter — Image restoration by Wiener filtering.

Warning

wiener_filterwiener_filterWienerFilterWienerFilterWienerFilter is obsolete and is only provided for reasons of backward compatibility.

Signature

wiener_filter(Image, Psf, FilteredImage : RestoredImage : : )

Herror wiener_filter(const Hobject Image, const Hobject Psf, const Hobject FilteredImage, Hobject* RestoredImage)

Herror T_wiener_filter(const Hobject Image, const Hobject Psf, const Hobject FilteredImage, Hobject* RestoredImage)

void WienerFilter(const HObject& Image, const HObject& Psf, const HObject& FilteredImage, HObject* RestoredImage)

HImage HImage::WienerFilter(const HImage& Psf, const HImage& FilteredImage) const

static void HOperatorSet.WienerFilter(HObject image, HObject psf, HObject filteredImage, out HObject restoredImage)

HImage HImage.WienerFilter(HImage psf, HImage filteredImage)

Description

wiener_filterwiener_filterWienerFilterWienerFilterWienerFilter produces an estimate of the original image (= image without noise and blurring) by minimizing the mean square error between estimated and original image. wiener_filterwiener_filterWienerFilterWienerFilterWienerFilter can be used to restore images corrupted by noise and/or blurring (e.g. motion blur, atmospheric turbulence or out-of-focus blur). Method and realisation of this restoration technique bases on the following model: The corrupted image is interpreted as the output of a (disturbed) linear system. Functionality of a linear system is determined by its specific impuls response. So the convolution of original image and impulse response results in the corrupted image. The specific impulse response describes image acquisition and the occured degradations. In the presence of additive noise an additional noise term must be considered. So the corrupted image can be modeled as the result of

          [convolution(impulse_response,original_image)] + noise_term
The noise term encloses two different terms describing image-dependent and image-independent noise. According to this model, two terms must be known for restoration by Wiener filtering:

  1. degradation-specific impulse response

  2. noise term

So wiener_filterwiener_filterWienerFilterWienerFilterWienerFilter needs a smoothed version of the input image to estimate the power spectral density of noise and original image. One can use one of the smoothing HALCON-filters (e.g. eliminate_min_maxeliminate_min_maxEliminateMinMaxEliminateMinMaxEliminateMinMax) to get this version. wiener_filterwiener_filterWienerFilterWienerFilterWienerFilter needs further the impulse response that describes the specific degradation. This impulse response (represented in spatial domain) must fit into an image of HALCON image type 'real'. There exist two HALCON-operators for generation of an impulse response for motion blur and out-of-focus (see gen_psf_motiongen_psf_motionGenPsfMotionGenPsfMotionGenPsfMotion, gen_psf_defocusgen_psf_defocusGenPsfDefocusGenPsfDefocusGenPsfDefocus). The representation of the impulse response presumes the origin in the upper left corner. This results in the following disposition of an NxM sized image:

wiener_filterwiener_filterWienerFilterWienerFilterWienerFilter works as follows:

The result image has got image type 'real'.

Attention

PsfPsfPsfPsfpsf must be of image type 'real' and conform to ImageImageImageImageimage and FilteredImageFilteredImageFilteredImageFilteredImagefilteredImage in image width and height.

Execution Information

Parameters

ImageImageImageImageimage (input_object)  (multichannel-)image objectHImageHImageHobject (byte / direction / cyclic / int1 / int2 / uint2 / int4 / real)

Corrupted image.

PsfPsfPsfPsfpsf (input_object)  (multichannel-)image objectHImageHImageHobject (real)

impulse response (PSF) of degradation (in spatial domain).

FilteredImageFilteredImageFilteredImageFilteredImagefilteredImage (input_object)  (multichannel-)image objectHImageHImageHobject (byte / direction / cyclic / int1 / int2 / uint2 / int4 / real)

Smoothed version of corrupted image.

RestoredImageRestoredImageRestoredImageRestoredImagerestoredImage (output_object)  image objectHImageHImageHobject * (real)

Restored image.

Example (C)

/* Restoration of a noisy image (size=256x256), that was blurred by motion*/
Hobject object;
Hobject restored;
Hobject psf;
Hobject noisefiltered;
/* 1. Generate a Point-Spread-Function for a motion-blur with       */
/*    parameter a=10 and direction along the x-axis                 */
gen_psf_motion(&psf,256,256,10,0,3);
/* 2. Noisefiltering of the image                                   */
median_image(object,&noisefiltered,"circle",2,0);
/* 3. Wiener-filtering                                              */
wiener_filter(object,psf,noisefiltered,&restored);

Result

wiener_filterwiener_filterWienerFilterWienerFilterWienerFilter returns 2 (H_MSG_TRUE) if all parameters are correct. If the input is empty wiener_filterwiener_filterWienerFilterWienerFilterWienerFilter returns with an error message.

Possible Predecessors

gen_psf_motiongen_psf_motionGenPsfMotionGenPsfMotionGenPsfMotion, simulate_motionsimulate_motionSimulateMotionSimulateMotionSimulateMotion, simulate_defocussimulate_defocusSimulateDefocusSimulateDefocusSimulateDefocus, gen_psf_defocusgen_psf_defocusGenPsfDefocusGenPsfDefocusGenPsfDefocus, optimize_fft_speedoptimize_fft_speedOptimizeFftSpeedOptimizeFftSpeedOptimizeFftSpeed

Alternatives

wiener_filter_niwiener_filter_niWienerFilterNiWienerFilterNiWienerFilterNi

See also

simulate_motionsimulate_motionSimulateMotionSimulateMotionSimulateMotion, gen_psf_motiongen_psf_motionGenPsfMotionGenPsfMotionGenPsfMotion, simulate_defocussimulate_defocusSimulateDefocusSimulateDefocusSimulateDefocus, gen_psf_defocusgen_psf_defocusGenPsfDefocusGenPsfDefocusGenPsfDefocus

References

M. Lückenhaus:“Grundlagen des Wiener-Filters und seine Anwendung in der Bildanalyse”; Diplomarbeit; Technische Universität München, Institut für Informatik; Lehrstuhl Prof. Radig; 1995
Azriel Rosenfeld, Avinash C. Kak: Digital Picture Processing, Computer Science and Aplied Mathematics, Academic Press New York/San Francisco/London 1982

Module

Foundation


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