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Benefiting from Multitask Learning to Improve Single Image Super-Resolution
[article]
2019
arXiv
pre-print
Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are mostly based on optimizing pixel and content wise similarity between recovered and high-resolution (HR) images and do not benefit from recognizability of semantic classes. In this paper, we introduce a novel approach using categorical
arXiv:1907.12488v1
fatcat:knhdqe6to5eujmii4p6xflupx4