A majorize-minimize approach for high-quality depth upsampling

Youngjung Kim, Sunghwan Choi, Changjae Oh, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


This paper describes a non-convex model that is carefully designed for high quality depth upsampling. Modern depth sensors such as time-of-flight cameras provide a promising depth measurement with video rate, but suffer from noise and low resolution. To tackle these limitations, we formulate an optimization problem using a robust potential function. In this formulation, a nonlocal principle established in the high-dimensional feature space is used to disambiguate the up-sampling problem. We also derive a numerical algorithm based on the majorization-minimization approach for efficient optimization. The proposed model iteratively creates a new affinity space that determines the influence of neighboring pixels by jointly considering spatial distance, appearance, and current estimates. This behavior enables one to significantly reduce annoying artifacts on a variety of range dataset, including a challenging real measurement. Extensive experiments demonstrate that the proposed model achieves competitive performance with state-of-the-art methods.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479983391
Publication statusPublished - 2015 Dec 9
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 2015 Sept 272015 Sept 30

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


OtherIEEE International Conference on Image Processing, ICIP 2015
CityQuebec City

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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