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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Takeda, Hiroyuki
|e verfasserin
|4 aut
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| 245 |
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|a Kernel regression for image processing and reconstruction
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|c 2007
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 28.02.2007
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|a Date Revised 26.10.2019
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|a published: Print
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|a Citation Status MEDLINE
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|a In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Farsiu, Sina
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Milanfar, Peyman
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 16(2007), 2 vom: 21. Feb., Seite 349-66
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|g volume:16
|g year:2007
|g number:2
|g day:21
|g month:02
|g pages:349-66
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