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Multi-scale deep neural networks for real image super-resolution
[article]
2019
arXiv
pre-print
Single image super-resolution (SR) is extremely difficult if the upscaling factors of image pairs are unknown and different from each other, which is common in real image SR. To tackle the difficulty, we develop two multi-scale deep neural networks (MsDNN) in this work. Firstly, due to the high computation complexity in high-resolution spaces, we process an input image mainly in two different downscaling spaces, which could greatly lower the usage of GPU memory. Then, to reconstruct the details
arXiv:1904.10698v1
fatcat:jdrbcz7egze6daqlussy36adyu