The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution
Abstract
Most modern face super-resolution methods resort to convolutional neural networks (CNN) to infer highresolution (HR) face images. When dealing with very low resolution (LR) images, the performance of these CNN based methods greatly degrades. Meanwhile, these methods tend to produce over-smoothed outputs and miss some textural details. To address these challenges, this paper presents 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 (2×, 4×, 8× and even 16×) in a unified framework. Different from conventional CNN methods directly inferring HR images, our approach firstly learns to predict the LR's corresponding series of HR's wavelet coefficients before reconstructing HR images from them. To capture both global topology information and local texture details of human faces, we present a flexible and extensible convolutional neural network with three types of loss: wavelet prediction loss, texture loss and full-image loss. Extensive experiments demonstrate that the proposed approach achieves more appealing results both quantitatively and qualitatively than state-ofthe- art super-resolution methods.
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Cites
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Cited by
Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058
On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques
Li, P., Prieto, L., Mery, D., & Flynn, P. J. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2890812 · Cited by 171
NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results
Li, Y., Zhang, Y., Timofte, R., Van Gool, L., Yu, L., Li, Y., Li, X., Jiang, T., Wu, Q., Han, M., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00189 · Cited by 72
Multibranch Wavelet-Based Network for Image Demoiréing
Yeh, C.-H., Lo, C., & He, C.-H. (2024) · Sensors · doi:10.3390/s24092762 · Cited by 7
LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models
Sadat, S., Buhmann, J., Bradley, D., Hilliges, O., & Weber, R. M. (2024) · arXiv · doi:10.48550/arxiv.2405.14477 · Cited by 7
Metadata
- Authors
- Huang, H., He, R., Sun, Z., & Tan, T.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1698-1706
- OpenAlex
- W2776107444
- Cited by
- 518
- References
- 34
- FWCI
- 9.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Huang, H., He, R., Sun, Z., & Tan, T. (2017). Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.187
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