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Proceedings Paper

Super-resolution restoration of motion blurred images
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Paper Abstract

In this paper, we investigate super-resolution image restoration from multiple images, which are possibly degraded with large motion blur. The blur kernel for each input image is separately estimated. This is unlike many existing super-resolution algorithms, which assume identical blur kernel for all input images. We also do not make any restrictions on the motion fields among images; that is, we estimate dense motion field without simplifications such as parametric motion. We present a two-step algorithm: In the first step, each input image is deblurred using the estimated blur kernel. In the second step, super-resolution restoration is applied to the deblurred images. Because the estimated blur kernels may not be accurate, we propose a weighted cost function for the super-resolution restoration step, where a weight associated with an input image reflects the reliability of the corresponding kernel estimate and the deblurred image. We provide experimental results from real video data captured with a hand-held camera, and show that the proposed weighting scheme is robust to motion deblurring errors.

Paper Details

Date Published: 7 March 2014
PDF: 9 pages
Proc. SPIE 9023, Digital Photography X, 90230F (7 March 2014); doi: 10.1117/12.2038844
Show Author Affiliations
Qinchun Qian, Louisiana State Univ. (United States)
Bahadir K. Gunturk, Louisiana State Univ. (United States)
Istanbul Medipol Univ. (Turkey)


Published in SPIE Proceedings Vol. 9023:
Digital Photography X
Nitin Sampat; Radka Tezaur; Sebastiano Battiato; Boyd A. Fowler, Editor(s)

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