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Proceedings Paper

Structure-aware depth super-resolution using Gaussian mixture model
Author(s): Sunok Kim; Changjae Oh; Youngjung Kim; Kwanghoon Sohn
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Paper Abstract

This paper presents a probabilistic optimization approach to enhance the resolution of a depth map. Conventionally, a high-resolution color image is considered as a cue for depth super-resolution under the assumption that the pixels with similar color likely belong to similar depth. This assumption might induce a texture transferring from the color image into the depth map and an edge blurring artifact to the depth boundaries. In order to alleviate these problems, we propose an efficient depth prior exploiting a Gaussian mixture model in which an estimated depth map is considered to a feature for computing affinity between two pixels. Furthermore, a fixed-point iteration scheme is adopted to address the non-linearity of a constraint derived from the proposed prior. The experimental results show that the proposed method outperforms state-of-the-art methods both quantitatively and qualitatively.

Paper Details

Date Published: 17 March 2015
PDF: 9 pages
Proc. SPIE 9393, Three-Dimensional Image Processing, Measurement (3DIPM), and Applications 2015, 93930J (17 March 2015); doi: 10.1117/12.2078795
Show Author Affiliations
Sunok Kim, Yonsei Univ. (Korea, Republic of)
Changjae Oh, Yonsei Univ. (Korea, Republic of)
Youngjung Kim, Yonsei Univ. (Korea, Republic of)
Kwanghoon Sohn, Yonsei Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 9393:
Three-Dimensional Image Processing, Measurement (3DIPM), and Applications 2015
Robert Sitnik; William Puech, Editor(s)

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