Large-scale blind face super-resolution via Edge-guided Frequency-aware Generative Facial Prior Networks
Author(s)
Cheng, Xi
Author(s)
Yang, J.
Date Issued
2023
Publisher
IEEE
Related Publication(s)
Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2022)
Start page
1638
End page
1643
Abstract
Large-scale blind face super-resolution is a significant image processing task with high practical value and wide applications. The task is more challenging than conventional image super-resolution since the degradation is more complex and the amount of useful information is very small inside the image. To address this problem, we propose Edge-guided Frequency-aware Generative Prior Network (EFGPN), a super-resolution method based on edge guidance, frequency constraint, and generative priors. The proposed edge guidance could aid the network in retaining more structural information. Frequency constraints suppress artifacts while allowing the network to generate good features across all frequency bands. The use of generative priors enables the network to produce photorealistic faces. The proposed EFGPN can blindly super-resolve low-resolution faces to high-quality faces. Experimental results indicate that our proposed EFGPN outperforms the state-of-the-arts under subjective and objective evaluations.
SFU Affiliated Publication
Yes
Availability at SFU Library
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