Multi-Image Super-Resolution Using a Locally Adaptive Denoising-Based Refinement

Bätz M, Koloda J, Eichenseer A, Kaup A (2016)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2016

Event location: Montreal

ISBN: 978-1-5090-3724-7

DOI: 10.1109/MMSP.2016.7813343

Abstract

Spatial resolution enhancement is of particular interest in many applications such as entertainment, surveillance, or automotive systems. Besides using a more expensive, higher resolution sensor, it is also possible to apply super-resolution techniques on the low resolution content. Super-resolution methods can be basically classified into single-image and multi-image super-resolution. In this paper, we propose the integration of a novel locally adaptive de noising-based refinement step as an intermediate processing step in a multi-image super-resolution framework. The idea is to be capable of removing reconstruction artifacts while preserving the details in areas of interest such as text. Simulation results show an average gain in luminance PSNR of up to 0.2 dB and 0.3 dB for an up scaling of 2 and 4, respectively. The objective results are substantiated by the visual impression.

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How to cite

APA:

Bätz, M., Koloda, J., Eichenseer, A., & Kaup, A. (2016). Multi-Image Super-Resolution Using a Locally Adaptive Denoising-Based Refinement. In Proceedings of the IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). Montreal, CA.

MLA:

Bätz, Michel, et al. "Multi-Image Super-Resolution Using a Locally Adaptive Denoising-Based Refinement." Proceedings of the IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), Montreal 2016.

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