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A multiresolution mixture generative adversarial network for video super-resolution
2020
PLoS ONE
Generative adversarial networks (GANs) have been used to obtain super-resolution (SR) videos that have improved visual perception quality and more coherent details. However, the latest methods perform poorly in areas with dense textures. To better recover the areas with dense textures in video frames and improve the visual perception quality and coherence in videos, this paper proposes a multiresolution mixture generative adversarial network for video super-resolution (MRMVSR). We propose a
doi:10.1371/journal.pone.0235352
pmid:32649694
fatcat:5pam5g32pzcajh7dft7rasmnya