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Proceedings Paper

Motion adaptive Kalman filter for super-resolution
Author(s): Martin Richter; Fabian Nasse; Hartmut Schröder
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Paper Abstract

Superresolution is a sophisticated strategy to enhance image quality of both low and high resolution video, performing tasks like artifact reduction, scaling and sharpness enhancement in one algorithm, all of them reconstructing high frequency components (above Nyquist frequency) in some way. Especially recursive superresolution algorithms can fulfill high quality aspects because they control the video output using a feed-back loop and adapt the result in the next iteration. In addition to excellent output quality, temporal recursive methods are very hardware efficient and therefore even attractive for real-time video processing. A very promising approach is the utilization of Kalman filters as proposed by Farsiu et al. Reliable motion estimation is crucial for the performance of superresolution. Therefore, robust global motion models are mainly used, but this also limits the application of superresolution algorithm. Thus, handling sequences with complex object motion is essential for a wider field of application. Hence, this paper proposes improvements by extending the Kalman filter approach using motion adaptive variance estimation and segmentation techniques. Experiments confirm the potential of our proposal for ideal and real video sequences with complex motion and further compare its performance to state-of-the-art methods like trainable filters.

Paper Details

Date Published: 31 January 2011
PDF: 9 pages
Proc. SPIE 7882, Visual Information Processing and Communication II, 78820A (31 January 2011); doi: 10.1117/12.872112
Show Author Affiliations
Martin Richter, Technische Univ. Dortmund (Germany)
Fabian Nasse, Technische Univ. Dortmund (Germany)
Hartmut Schröder, Technische Univ. Dortmund (Germany)

Published in SPIE Proceedings Vol. 7882:
Visual Information Processing and Communication II
Amir Said; Onur G. Guleryuz; Robert L. Stevenson, Editor(s)

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