A Unified Bayesian Approach to Multi-Frame Super-Resolution and Single-Image Upsampling in Multi-Sensor Imaging

Thomas Köhler, Johannes Jordan, Andreas Maier and Joachim Hornegger

Abstract

For a variety of multi-sensor imaging systems, there is a strong need for resolution enhancement. In this paper, we propose a unified method for single-image upsampling and multi-frame super-resolution of multi-channel images. We derive our algorithm from a Bayesian model that is formulated by a novel image prior to exploit sparsity of individual channels as well as a locally linear regression between the complementary channels. The reconstruction of high-resolution multi-channel images from low-resolution ones and the estimation of associated hyperparameters to define our prior model is formulated as a joint energy minimization. We introduce an alternating minimization scheme to solve this non-convex optimization problem efficiently. Our framework is applicable to various types of multi-sensor setups that are addressed in our experimental evaluation, including color, multispectral and 3-D range imaging. Comprehensive qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art algorithms.

Session

Poster 2

Files

Extended Abstract (PDF, 1619K)
Paper (PDF, 2M)

DOI

10.5244/C.29.143
https://dx.doi.org/10.5244/C.29.143

Citation

Bibtex

@inproceedings{BMVC2015_143,
	title={A Unified Bayesian Approach to Multi-Frame Super-Resolution and Single-Image Upsampling in Multi-Sensor Imaging},
	author={Thomas Köhler and Johannes Jordan and Andreas Maier and Joachim Hornegger},
	year={2015},
	month={September},
	pages={143.1-143.12},
	articleno={143},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.143},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.143}
}