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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:1057-7149
1941-0042
DOI:10.1109/83.923279