Face Super-resolution Guided by Facial Component Heatmaps

ECCV 2018 Xin YuBasura FernandoBernard GhanemFatih PorikliRichard Hartley

State-of-the-art face super-resolution methods use deep convolutional neural networks to learn a mapping between low-resolution (LR) facial patterns and their corresponding high-resolution (HR) counterparts by exploring local information. However, most of them do not account for face structure and suffer from degradations due to large pose variations and misalignments of faces... (read more)

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