Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Enhancement of dynamic depth scenes by upsampling for precise super-resolution (UP-SR)
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.ORCID iD: 0000-0003-2298-6774
2016 (English)In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 147, p. 38-49Article in journal (Refereed) Published
Abstract [en]

Multi-frame super-resolution is the process of recovering a high resolution image or video from a set of captured low resolution images. Super-resolution approaches have been largely explored in 2-D imaging. However, their extension to depth videos is not straightforward due to the textureless nature of depth data, and to their high frequency contents coupled with fast motion artifacts. Recently, few attempts have been introduced where only the super-resolution of static depth scenes has been addressed. In this work, we propose to enhance the resolution of dynamic depth videos with non-rigidly moving objects. The proposed approach is based on a new data model that uses densely upsampled, and cumulatively registered versions of the observed low resolution depth frames. We show the impact of upsampling in increasing the sub-pixel accuracy and reducing the rounding error of the motion vectors. Furthermore, with the proposed cumulative motion estimation, a high registration accuracy is achieved between non-successive upsampled frames with relative large motions. A statistical performance analysis is derived in terms of mean square error explaining the effect of the number of observed frames and the effect of the super-resolution factor at a given noise level. We evaluate the accuracy of the proposed algorithm theoretically and experimentally as function of the SR factor, and the level of noise contamination. Experimental results on both real and synthetic data show the effectiveness of the proposed algorithm on dynamic depth videos as compared to state-of-art methods. © 2016 Elsevier Inc.

Place, publisher, year, edition, pages
Academic Press Inc Elsevier , 2016. Vol. 147, p. 38-49
Keywords [en]
Super-resolution, Moving objects, Non-rigid motion, Depth video, Upsampling, Cumulative motion
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-259142DOI: 10.1016/j.cviu.2016.04.006ISI: 000377052000004Scopus ID: 2-s2.0-84964342343OAI: oai:DiVA.org:kth-259142DiVA, id: diva2:1350613
Note

QC 20191025

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-10-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopushttp://www.sciencedirect.com/science/article/pii/S1077314216300303

Search in DiVA

By author/editor
Ottersten, Björn
In the same journal
Computer Vision and Image Understanding
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 13 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf