A generalization of median filtering using linear combinations of order statistics

Bovik, Alan C.; Huang, Thomas S.; Munson, D.C.

doi:10.1109/tassp.1983.1164247

Cited by 525 publications

(157 citation statements)

References 12 publications

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“…Also note that the OWA operators correspond, with a different semantics, to an important class of nonlinear filters in signal and image processing: the order statistics based L-filters [30]. Ranking the data pushes aberrant values to the extreme positions away.…”

Section: ) Order Weighted Averaging Operatormentioning

confidence: 99%

Fuzzy fusion techniques for linear features detection in multitemporal SAR images

Chanussot¹,

Mauris²,

Lambert³

1999

IEEE Trans. Geosci. Remote Sensing

145

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Abstract-This paper is concerned with the automatic detection of linear features in SAR satellite data, with application to road network extraction. After a directional prefiltering step, a morphological line detector is presented. To improve the detection performances, the results obtained on multitemporal data are fused. Different fusion strategies involving different fusion operators are then presented. Since extensions of classical set union and intersection do not lead to satisfactory results (the corresponding operators are either too indulgent or too severe), the first strategy consists of fusing the data using a compromise operator. The second strategy consists of fusing the results computed with two operators that have opposite properties, in order to obtain a final intermediate result. Thanks to the wide range of properties they provide, fuzzy operators are used to test and compare these two fusion strategies on real ERS-1 multitemporal data.

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Section: ) Order Weighted Averaging Operatormentioning

confidence: 99%

Fuzzy fusion techniques for linear features detection in multitemporal SAR images

Chanussot¹,

Mauris²,

Lambert³

1999

IEEE Trans. Geosci. Remote Sensing

145

View full text Add to dashboard Cite

show abstract

“…In fact, the median filter uses the order statistics information, and the noisy values are regarded as outliers so that they can be removed. The further research about the median filter has led to a category of nonlinear filters that produce outputs based on the rank-ordered observations, such as order statistic (OS) filters [2,3]. Such filters based on only order statistics have some advantages over linear filters.…”

Section: Categories Of Nonlinear Filtersmentioning

confidence: 99%

“…However, the rank ordering in the hybrid filter only gives some indications of the pixel similarity, that is, the similarity has been heavily quantized. In order to incorporate the complete similarity information as the original bilateral filter does, we obtain the vector X s = x [1] , x [2] , . .…”

Section: Trained Bilateral Filtermentioning

confidence: 99%

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Adding explicit content classification to nonlinear filters

Haan

2011

SIViP

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Nonlinear filters are known for better edgepreserving performance in image processing applications as they can adapt to some local image content. Instead of trying to find a single optimal filter that can adapt to all the image content, some classification-based approaches first apply a pre-classification on the image content and then employ an optimal linear filter for each content class. It is interesting to extend the linear filter in such approaches to a nonlinear filter and see if the explicit content classification, can still add to such inherently adapting nonlinear filters. In this paper, we investigate several categories of nonlinear filters: order statistics filters, hybrid filters, neural filters, and bilateral filters with different forms of content classification in various image processing applications, including image de-blocking, noise reduction, and image interpolation.

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“…Median and rank order filters are nonlinear TI discrete filters that can suppress impulse (or speckle) noise from signals while preserving their edges; they have been used extensively for image and signal enhancement [42]- [45].…”

Section: B Median and Rank Order Filtersmentioning

confidence: 99%

A representation theory for morphological image and signal processing

Maragos

1989

IEEE Trans. Pattern Anal. Machine Intell.

204

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Abstract-This paper presents a unifying theory for many concepts and operations encountered in or related to morphological image and signal analysis. This unification requires a set-theoretic melhodology, where signals are modeled as sets, systems (signal transformations) are viewed as set mappings, and translation-invariant systems are uniquely characterized by special collections of input signals. This approach leads to a general representation theory, in which any translation-invariant, increasing, upper semicontinuous system can be represented exactly as a minimal nonlinear superposition of morphological erosions or dilations.In this representation, many similarities and a few differences are observed between systems processing binary or multilevel signals, and continuous-domain or discrete-domain signals.The theory is used to analyze some special cases of image/signal analysis systems, such as morphological filters, median and order-statistic filters, linear filters, and shape recognition transforms.Although the developed theory is algebraic, its prototype operations are well suitable for shape analysis; hence, the results of this study also apply to systems that extract information about the geometrical structure of signals.Zndex Terms--Image/signal processing, mathematical morphology, nonlinear/linear filtering, shape analysis, systems representation.

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A generalization of median filtering using linear combinations of order statistics

Cited by 525 publications

References 12 publications

Fuzzy fusion techniques for linear features detection in multitemporal SAR images

Fuzzy fusion techniques for linear features detection in multitemporal SAR images

Adding explicit content classification to nonlinear filters

A representation theory for morphological image and signal processing

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