Super-resolution restoration of motion blurred images
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CitationQian, Q. ve Güntürk, B. K. (2014). Super-resolution restoration of motion blurred images. Conference on Digital Photography X, San Francisco, CA, 03-05 Şubat 2014. https://dx.doi.org/10.1117/12.2038844
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.