Hybrid Super-Resolution Combining Example-Based Single-Image and Interpolation-Based Multi-Image Reconstruction Approaches

Bätz M, Eichenseer A, Seiler J, Jonscher M, Kaup A (2015)


Publication Language: English

Publication Status: Published

Publication Type: Conference contribution

Publication year: 2015

Journal

Pages Range: 58-62

Event location: Quebec City CA

ISBN: 978-1-4799-8339-1

DOI: 10.1109/ICIP.2015.7350759

Abstract

Achieving a higher spatial resolution is of particular interest in many applications such as video surveillance and can be realized by employing higher resolution sensors or applying super-resolution methods. Traditional super-resolution algorithms are based on either a single low resolution image or on multiple low resolution frames. In this paper, a hybrid super-resolution method is proposed which combines both a single-image and a multi-image approach using a soft decision mask. The mask is computed from the motion information utilized in the multi-image super-resolution part. This concept is shown to work for one particular setup but is also extensible toward other combinations of single-image and multi-image super-resolution algorithms as well as other merging metrics. Simulation results show an average luminance PSNR gain of up to 0.85 dB and 0.59 dB for upscaling factors of 2 and 4, respectively. Visual results substantiate the objective results.

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How to cite

APA:

Bätz, M., Eichenseer, A., Seiler, J., Jonscher, M., & Kaup, A. (2015). Hybrid Super-Resolution Combining Example-Based Single-Image and Interpolation-Based Multi-Image Reconstruction Approaches. In Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP) (pp. 58-62). Quebec City, CA.

MLA:

Bätz, Michel, et al. "Hybrid Super-Resolution Combining Example-Based Single-Image and Interpolation-Based Multi-Image Reconstruction Approaches." Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City 2015. 58-62.

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