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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2803306
|2 doi
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|a pubmed24n0953.xml
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|a DE-627
|b ger
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Liu, Jing
|e verfasserin
|4 aut
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| 245 |
1 |
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|a IPAD
|b Intensity Potential for Adaptive De-Quantization
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| 264 |
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 30.07.2018
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| 500 |
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|a Date Revised 30.07.2018
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Display devices at bit depth of 10 or higher have been mature but the mainstream media source is still at bit depth of eight. To accommodate the gap, the most economic solution is to render source at low bit depth for high bit-depth display, which is essentially the procedure of de-quantization. Traditional methods, such as zero-padding or bit replication, introduce annoying false contour artifacts. To better estimate the least-significant bits, later works use filtering or interpolation approaches, which exploit only limited neighbor information, cannot thoroughly remove the false contours. In this paper, we propose a novel intensity potential (IP) field to model the complicated relationships among pixels. The potential value decreases as the spatial distance to the field source increases and the potentials from different field sources are additive. Based on the proposed IP field, an adaptive de-quantization procedure is then proposed to convert low-bit-depth images to high-bit-depth ones. To the best of our knowledge, this is the first attempt to apply potential field for natural images. The proposed potential field preserves local consistency and models the complicated contexts well. Extensive experiments on natural, synthetic, and high-dynamic range image data sets validate the efficiency of the proposed IP field. Significant improvements have been achieved over the state-of-the-art methods on both the peak signal-to-noise ratio and the structural similarity
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Zhai, Guangtao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Anan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Xiaokang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Xibin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Chang Wen
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 10 vom: 03. Okt., Seite 4860-4872
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:27
|g year:2018
|g number:10
|g day:03
|g month:10
|g pages:4860-4872
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TIP.2018.2803306
|3 Volltext
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