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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2019.2894627
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhang, Benxuan
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Depth of Field Rendering Using Multilayer-Neighborhood Optimization
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| 264 |
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1 |
|c 2020
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 01.07.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Depth of field (DOF) is utilized widely to deliver artistic effects in photography. However, existing post-processing techniques for rendering DOF effects introduce visual artifacts such as color leakage, blurring discontinuity, and the partial occlusion problems which limit the application of DOF. Traditionally, occluded pixels are ignored or not well estimated although they might make key contributions to images. In this paper, we propose a new filtering approach which takes approximated occluded pixels into account to synthesize the DOF effects for images. In our approach, images are separated into different layers based on depth. Besides, we utilize adaptive PatchMatch method to estimate the intensities of occluded pixels, especially in the background region. We again propose a new multilayer-neighborhood optimization to estimate occluded pixels contributions and render the images. Finally, we apply gathering filter to achieve the rendered images with elite DOF effects. Multiple experiments have shown that our approach can handle color leakage, blurring discontinuity and partial occlusion problem while providing high-quality DOF rendering effects
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Sheng, Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Ping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lee, Tong-Yee
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 26(2020), 8 vom: 23. Aug., Seite 2546-2559
|w (DE-627)NLM098269445
|x 1941-0506
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| 773 |
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|g volume:26
|g year:2020
|g number:8
|g day:23
|g month:08
|g pages:2546-2559
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|u http://dx.doi.org/10.1109/TVCG.2019.2894627
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