A revisit to MRF-based depth map super-resolution and enhancement

Jiangbo Lu, Dongbo Min, Ramanpreet Singh Pahwa, Minh N. Do

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

88 Scopus citations

Abstract

This paper presents a Markov Random Field (MRF)-based approach for depth map super-resolution and enhancement. Given a low-resolution or moderate quality depth map, we study the problem of enhancing its resolution or quality with a registered high-resolution color image. Different from the previous methods, this MRF-based approach is based on a novel data term formulation that fits well to the unique characteristics of depth maps. We also discuss a few important design choices that boost the performance of general MRF-based methods. Experimental results show that our proposed approach achieves high-resolution depth maps at more desirable quality, both qualitatively and quantitatively. It can also be applied to enhance the depth maps derived with state-of-the-art stereo methods, resulting in the raised ranking based on the Middlebury benchmark.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages985-988
Number of pages4
DOIs
StatePublished - 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • depth super-resolution
  • depth-enhancement
  • global optimization
  • MRFs
  • Time-of-flight sensor

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