Self-Supervised Deep Blind Video Super-Resolution. (arXiv:2201.07422v1 [cs.CV])

Existing deep learning-based video super-resolution (SR) methods usually
depend on the supervised learning approach, where the training data is usually
generated by the blurring operation with known or predefined kernels (e.g.,
Bicubic kernel) followed by a decimation operation. However, this does not hold
for real applications as the degradation process is complex and cannot be
approximated by these idea cases well. Moreover, obtaining high-resolution (HR)
videos and the corresponding low-resolution (LR) ones in real-world scenarios
is difficult. To overcome these …

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