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Probabilistic Multiview Depth Image Enhancement Using Variational Inference
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-7807-5681
2015 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 9, no 3, 435-448 p.Article in journal (Refereed) Published
Abstract [en]

An inference-based multiview depth image enhancement algorithm is introduced and investigated in this paper. Multiview depth imagery plays a pivotal role in free-viewpoint television. This technology requires high-quality virtual view synthesis to enable viewers to move freely in a dynamic real world scene. Depth imagery of different viewpoints is used to synthesize an arbitrary number of novel views. Usually, the depth imagery is estimated individually by stereo-matching algorithms and, hence, shows inter-view inconsistency. This inconsistency affects the quality of view synthesis negatively. This paper enhances the multiview depth imagery at multiple viewpoints by probabilistic weighting of each depth pixel. First, our approach classifies the color pixels in the multiview color imagery. Second, using the resulting color clusters, we classify the corresponding depth values in the multiview depth imagery. Each clustered depth image is subject to further subclustering. Clustering based on generative models is used for assigning probabilistic weights to each depth pixel. Finally, these probabilistic weights are used to enhance the depth imagery at multiple viewpoints. Experiments show that our approach consistently improves the quality of virtual views by 0.2 dB to 1.6 dB, depending on the quality of the input multiview depth imagery.

Place, publisher, year, edition, pages
2015. Vol. 9, no 3, 435-448 p.
Keyword [en]
Bayes methods, Cameras, Clustering algorithms, Image color analysis, Sensors, Signal processing algorithms, Vectors, Dirichlet mixture model, Multiview video, free-viewpoint television, multiview depth consistency, virtual view synthesis
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-163409DOI: 10.1109/JSTSP.2014.2373331ISI: 000351749800006OAI: oai:DiVA.org:kth-163409DiVA: diva2:800080
Note

QC 20150402

Available from: 2015-04-01 Created: 2015-04-01 Last updated: 2017-12-04Bibliographically approved

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Flierl, Markus

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