&lt;title&gt;Video signal processing using vector median&lt;/title&gt;

Oistamo, Kai; Neuvo, Y.

doi:10.1117/12.24133

Cited by 7 publications

(3 citation statements)

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“…In the restoration and enhancement of multispectral images, utilization of the correlation between different spectral components has been found to give good results [7,8,23,80). Vector median (VM) operation [7,8,25) processes multispectral signals as vector-valued data and, thus uses the correlation between different spectral components.…”

Section: Weighted Vector Median Operationmentioning

confidence: 99%

“…Multispectral signals can be processed componentwise by applying nominally identical filters to the different components separately. Componentwise processing does not utilize the correlation between different spectral components and may result in problems, especially around edges [8,80). False colors caused by edge shift are examples of these problems [80).…”

Section: Weighted Vector Median Operationmentioning

confidence: 99%

“…Componentwise processing does not utilize the correlation between different spectral components and may result in problems, especially around edges [8,80). False colors caused by edge shift are examples of these problems [80). In Figure 14.4(a), there is a noise corrupted input signal consisting of two color components: red and green.…”

Section: Weighted Vector Median Operationmentioning

confidence: 99%

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Motion Analysis and Image Sequence Processing

Sezan¹,

Lagendijk²

1993

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Section: Weighted Vector Median Operationmentioning

confidence: 99%

Section: Weighted Vector Median Operationmentioning

confidence: 99%

Section: Weighted Vector Median Operationmentioning

confidence: 99%

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Motion Analysis and Image Sequence Processing

Sezan¹,

Lagendijk²

1993

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An overview of median and stack filtering

Coyle

Gallagher

Gabbouj

1992

Circuits Systems and Signal Process

108

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Abstract. Within the last two decades a small group of researchers has built a useful, nontrivial theory of nonlinear signal processing around the median-related filters known as rank-order filters, order-statistic filters, weighted median filters, and stack filters. This required significant effort to overcome the bias, both in education and research, toward linear theory, which has been dominant since the days of Fourier, Laplace, and "Convolute."We trace the development of this theory of nonlinear filtering from its beginnings in the study of noise-removal properties and structural behavior of the median filter to the recently developed theory of optimal stack filtering.The theory of stack filtering provides a point of view which unifies many different filter classes, including morphological filters, so it is discussed in detail. Of particular importance is the way this theory has brought together, in a single analytical framework, both the estimation-based and the structural-based approaches to the design of these filters.Some recent applications of median and stack filters are provided to demonstrate the effectiveness of this approach to nonlinear filtering. They include: the design of an optimal stack filter for image restoration; the use of vector median filters to attenuate impulsive noise in color images and to eliminate cross luminance and cross color in TV images; and the use of median-based filters for image sequence coding, reconstruction, and scan rate conversion in normal TV and HDTV systems.

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