Super-resolution restoration of motion blurred images
2014
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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