Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes
Given a tiny face image, existing face hallucination methods aim at super-resolving its high-resolution (HR) counterpart by learning a mapping from an exemplar dataset. Since a low-resolution (LR) input patch may correspond to many HR candidate patches, this ambiguity may lead to distorted HR facial details and wrong attributes such as gender reversal. An LR input contains low-frequency facial components of its HR version while its residual face image, defined as the difference between the HR...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE/CVF Conference on Computer Vision and Pattern Recognition|
|Super-Resolving_Very_Low-Resolution_Face_Images_with_Supplementary_Attributes.pdf||1.51 MB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.