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Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.23024
Abstract: There is an increasing interest in enhancing the quality of low‐resolution (LR) facial images for various social life applications. Existing methods often use domain‐specific prior knowledge, which is effective in improving the face super‐resolution model's…
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Keywords:
face super;
cross scale;
face;
super resolution ... See more keywords
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Published in 2019 at "Neural Computing and Applications"
DOI: 10.1007/s00521-019-04652-5
Abstract: Face super-resolution is an example of super-resolution technique, where it takes one or multiple observed low-resolution images and then converts them to high-resolution image. Learning-based face super-resolution depends on prior information from training database. Most…
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Keywords:
resolution;
super resolution;
face super;
adaptive representation ... See more keywords
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Published in 2020 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-020-09072-5
Abstract: In real-world surveillance scenario, the face recognition (FR) systems pose a lot of challenges due to the captured low-resolution (LR) and noisy probe images. A new face super-resolution (SR) algorithm is proposed to design a…
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Keywords:
resolution;
low resolution;
face super;
face ... See more keywords
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Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.03.048
Abstract: Face super-resolution has become an indispensable part in security problems such as video surveillance and identification system, but the distortion in facial components is a main obstacle to overcoming the problems. To alleviate it, most…
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Keywords:
resolution;
super resolution;
face super;
identity ... See more keywords
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Published in 2017 at "IEEE Access"
DOI: 10.1109/access.2017.2717963
Abstract: Learning-based face super-resolution relies on obtaining accurate a priori knowledge from the training data. Representation-based approaches (e.g., sparse representation-based and neighbor embedding-based schemes) decompose the input images using sophisticated regularization techniques. They give reasonably good…
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Keywords:
super resolution;
face super;
low rank;
face ... See more keywords
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Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2921859
Abstract: Face super-resolved (SR) images aid human perception. The state-of-the-art face SR methods leverage the spatial location of facial components as prior knowledge. However, it remains a great challenge to generate natural textures. In this paper,…
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Keywords:
face;
component semantic;
face super;
semantic prior ... See more keywords
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Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2934078
Abstract: Despite the great progress of image super-resolution in recent years, face super-resolution has still much room to explore good visual quality while preserving original facial attributes for larger up-scaling factors. This paper investigates a new…
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Keywords:
resolution;
reference based;
face super;
super resolution ... See more keywords
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1
Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3143499
Abstract: Face super-resolution (FSR) is defined as the generation of high-resolution face images from low-resolution face images. Existing FSR approaches usually improve the performance by combining deep learning with additional tasks such as face parsing and…
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Keywords:
face super;
network;
face;
super resolution ... See more keywords
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Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3389702
Abstract: Face Super-resolution (FSR) models encounter a significant challenge related to extremely low-dimensional ( $16\times 16$ pixels) and degraded input images. This deficiency in crucial facial details within the low-level and intermediate levels of the FSR…
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Keywords:
inline formula;
face;
face super;
tex math ... See more keywords
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Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3492716
Abstract: Face super-resolution (FSR) is critical for bolstering intelligent security in Internet of Things (IoT) systems. Recent deep learning-driven FSR algorithms have attained remarkable progress. However, they always require separate model training and optimization for each…
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Keywords:
scale adaptive;
face super;
resolution;
scale ... See more keywords
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Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2022.3181828
Abstract: Existing face hallucination methods always achieve improved performance through regularizing the model with facial prior. Most of them always estimate facial prior information first and then leverage it to help the prediction of the target…
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Keywords:
face super;
face;
face image;
resolution face ... See more keywords