7 March 2014 Super-resolution restoration of motion blurred images
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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.
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Qinchun Qian, Qinchun Qian, Bahadir K. Gunturk, Bahadir K. Gunturk, "Super-resolution restoration of motion blurred images", Proc. SPIE 9023, Digital Photography X, 90230F (7 March 2014); doi: 10.1117/12.2038844; https://doi.org/10.1117/12.2038844

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