Conference contribution
(Original article)


Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging


Publication Details
Author(s): Ghesu F, Köhler T, Haase S, Hornegger J
Title edited volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Verlag
Publication year: 2014
Conference Proceedings Title: Pattern Recognition
Pages range: 000-000
ISSN: 1611-3349

Event details
Event: 36th German Conference on Pattern Recognition, GCPR 2014
Event location: Münster
Start date of the event: 02/09/2014
End date of the event: 05/09/2014

Abstract

In this paper, we augment multi-frame super-resolution with the concept of guided filtering for simultaneous upsampling of 3-D range data and complementary photometric information in hybrid range imaging. Our guided super-resolution algorithm is formulated as joint maximum a-posteriori estimation to reconstruct high-resolution range and photometric data. In order to exploit local correlations between both modalities, guided filtering is employed for regularization of the proposed joint energy function. For fast and robust image reconstruction, we employ iteratively re-weighted least square minimization embedded into a cyclic coordinate descent scheme. The proposed method was evaluated on synthetic datasets and real range data acquired with Microsoft’s Kinect. Our experimental evaluation demonstrates that our approach outperforms state-of-the-art range super-resolution algorithms while it also provides super-resolved photometric data.



How to cite
APA: Ghesu, F., Köhler, T., Haase, S., & Hornegger, J. (2014). Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging. In Pattern Recognition (pp. 000-000). Springer Verlag.

MLA: Ghesu, Florin C., et al. "Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging." Proceedings of the 36th German Conference on Pattern Recognition, GCPR 2014, Münster Springer Verlag, 2014. 000-000.

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