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Deep Blind Video Super-resolution
[article]
2020
arXiv
pre-print
Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration. However, this assumption does not hold for video SR and usually leads to over-smoothed super-resolved images. In this paper, we propose a deep convolutional neural network (CNN) model to solve video SR by a blur kernel modeling approach. The proposed deep CNN model consists of motion blur estimation, motion estimation,
arXiv:2003.04716v1
fatcat:dj5mpfitgjhxpgj6n6vevyvspa