Application of partition-based median type filters for suppressing noise in images

An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 10; H. 6; S. 829 - 836
Hauptverfasser:	Chen, Tao, Wu, Hong Ren
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.06.2001 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive filters Applied sciences Computer science Detection, estimation, filtering, equalization, prediction Exact sciences and technology Filtering Gaussian noise Image processing Impulses Information, signal and communications theory Least squares approximation Mathematical analysis Noise Noise robustness Nonlinear filters Pixels Position (location) Retarding Signal and communications theory Signal processing Signal, noise Software engineering Statistics Studies Telecommunications and information theory Vectors Vectors (mathematics) Performance evaluation Partition method Simulation Image processing Noise reduction Adaptive filter Median filter Optimization Non linear filtering Least mean squares methods
ISSN:	1057-7149, 1941-0042
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Abstract	An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise.
AbstractList	An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise.
Author	Tao Chen Hong Ren Wu
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Keywords	Performance evaluation Partition method Simulation Image processing Noise reduction Adaptive filter Median filter Optimization Non linear filtering Least mean squares methods
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SubjectTerms	Adaptive filters Applied sciences Computer science Detection, estimation, filtering, equalization, prediction Exact sciences and technology Filtering Gaussian noise Image processing Impulses Information, signal and communications theory Least squares approximation Mathematical analysis Noise Noise robustness Nonlinear filters Pixels Position (location) Retarding Signal and communications theory Signal processing Signal, noise Software engineering Statistics Studies Telecommunications and information theory Vectors Vectors (mathematics)
Title	Application of partition-based median type filters for suppressing noise in images
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