Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution

Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data. They usually take as ass...

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Hauptverfasser:	Umer, Rao Muhammad, Foresti, Gian Luca, Micheloni, Christian
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computer Vision and Pattern Recognition
Online-Zugang:	Volltext bestellen
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