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Published in 2020 at "Journal of Real-Time Image Processing"
DOI: 10.1007/s11554-020-01009-3
Abstract: Face hallucination (FH) aims to reconstruct high-resolution faces from low-resolution face inputs, making it significant to other face-related tasks. Different from general super resolution issue, it often requires facial priors other than general extracted features…
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Keywords:
ensemble network;
real time;
face hallucination;
attention ... See more keywords
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Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2877422
Abstract: Face hallucination refers an application-specific super-resolution (SR) which predicts high-resolution images from one or multiple low-resolution inputs. Learning-based SR algorithms infer latent HR images by the guidance of coexisted priors from training samples. Various regularization…
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Keywords:
group embedding;
face;
embedding face;
face hallucination ... See more keywords
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Published in 2020 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2018.2868891
Abstract: Face hallucination is a technique that reconstructs high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of the human face to…
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Keywords:
representation;
patch;
face;
face hallucination ... See more keywords
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Published in 2021 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2020.3046918
Abstract: Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR)…
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Keywords:
frontal face;
face;
face hallucination;
level ... See more keywords
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Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3106773
Abstract: Manifold learning-based face hallucination technologies have been widely developed during the past decades. However, the conventional learning methods always become ineffective in noise environment due to the least-square regression, which usually generates distorted representations for…
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Keywords:
regression;
representation;
face;
face hallucination ... See more keywords
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Published in 2021 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2021.3061312
Abstract: Existing face hallucination methods based on convolutional neural networks (CNNs) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in non-uniform…
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Keywords:
copy paste;
illumination;
face hallucination;
face ... See more keywords