Ghesu FC, Köhler T, Haase S, Hornegger J (2014)
Publication Type: Conference contribution, Original article
Publication year: 2014
Publisher: Springer Verlag
Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages Range: 000-000
Conference Proceedings Title: Pattern Recognition
Event location: Münster
URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2014/Ghesu14-GIS.pdf
DOI: 10.1007/978-3-319-11752-2_18
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.
APA:
Ghesu, F.-C., 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). Münster: Springer Verlag.
MLA:
Ghesu, Florin-Cristian, 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.
BibTeX: Download