# Exemplar Guided Face Image Super-Resolution Without Facial Landmarks

@article{Dogan2019ExemplarGF,
title={Exemplar Guided Face Image Super-Resolution Without Facial Landmarks},
author={Berk Dogan and Shuhang Gu and R. Timofte},
journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
year={2019},
pages={1814-1823}
}
• Published 2019
• Computer Science
• 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. [...] Key Method GWAInet is trained in an adversarial generative manner to produce the desired high quality perceptual image results. The utilization of the HR guiding image is realized via the use of a warper subnetwork that aligns its contents to the input image and the use of a feature fusion chain for the extracted features from the warped guiding image and the input image. In training…Expand
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#### References

SHOWING 1-10 OF 53 REFERENCES
Face Hallucination with Tiny Unaligned Images by Transformative Discriminative Neural Networks
• Computer Science
• AAAI
• 2017
An end-to-end transformative discriminative neural network devised for super-resolving unaligned and very small face images with an extreme upscaling factor of 8.5 and significantly outperforms the state-of-the-art. Expand
Ultra-Resolving Face Images by Discriminative Generative Networks
• Computer Science
• ECCV
• 2016
This work presents a discriminative generative network that can ultra-resolve a very low resolution face image of size $$16 \times 16$$ pixels to its $$8\times$$ larger version by reconstructing 64 pixels from a single pixel. Expand
Learning Warped Guidance for Blind Face Restoration
• Computer Science
• ECCV
• 2018
Experiments show that the GFRNet not only performs favorably against the state-of-the-art image and face restoration methods, but also generates visually photo-realistic results on real degraded facial images. Expand
Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution
• Computer Science
• 2017 IEEE International Conference on Computer Vision (ICCV)
• 2017
A wavelet-based CNN approach that can ultra-resolve a very low resolution face image of 16 × 16 or smaller pixelsize to its larger version of multiple scaling factors in a unified framework with three types of loss: wavelet prediction loss, texture loss and full-image loss is presented. Expand
Learning to Super-Resolve Blurry Face and Text Images
• Computer Science
• 2017 IEEE International Conference on Computer Vision (ICCV)
• 2017
This work presents an algorithm to directly restore a clear highresolution image from a blurry low-resolution input and introduces novel training losses that help recover fine details. Expand
Learning Face Hallucination in the Wild
• Computer Science
• AAAI
• 2015
A new method of face hallucination is presented, which can consistently improve the resolution of face images even with large appearance variations, based on a novel network architecture called Bi-channel Convolutional Neural Network (Bi-channel CNN). Expand
Deep Learning Face Attributes in the Wild
• Computer Science
• 2015 IEEE International Conference on Computer Vision (ICCV)
• 2015
A novel deep learning framework for attribute prediction in the wild that cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. Expand
Learning Face Representation from Scratch
• Computer Science
• ArXiv
• 2014
A semi-automatical way to collect face images from Internet is proposed and a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace is built, based on which a 11-layer CNN is used to learn discriminative representation and obtain state-of-theart accuracy on LFW and YTF. Expand
Super-Identity Convolutional Neural Network for Face Hallucination
This paper defines a super-identity loss to measure the identity difference between a hallucinated face and its corresponding high-resolution face within the hypersphere identity metric space and presents a domain-integrated training approach by constructing a robust identity metric for faces from these two domains. Expand
VGGFace2: A Dataset for Recognising Faces across Pose and Age
• Computer Science
• 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)
• 2018
A new large-scale face dataset named VGGFace2 is introduced, which contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject, and the automated and manual filtering stages to ensure a high accuracy for the images of each identity are described. Expand