Face super-resolution guided by facial component heatmaps
State-of-the-art face super-resolution methods leverage deep convolutional neural networks to learn a mapping between low-resolution (LR) facial patterns and their corresponding high-resolution (HR) counterparts by exploring local appearance information. However, most of these methods do not account for facial structure and suffer from degradations due to large pose variations and misalignments. In this paper, we propose a method that explicitly incorporates structural information of faces into...[Show more]
|Collections||ANU Research Publications|
|Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Book Title:||15th European Conference on Computer Vision, ECCV 2018|
|01_Yu_Face_super-resolution_guided_2018.pdf||1.43 MB||Adobe PDF|
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