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)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet