@inproceedings{
li2024efficient,
title={Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network},
author={wenjie li and Heng Guo and Xuannan Liu and Kongming Liang and Jiani Hu and Zhanyu Ma and Jun Guo},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=ZyQWfb95zE}
}
Face super-resolution aims to reconstruct a high-resolution face image from a low-resolution face image. Previous methods typically employ an encoder-decoder structure to extract facial structural features, where the direct downsampling inevitably introduces distortions, especially to high-frequency features such as edges. To address this issue, we propose a wavelet-based feature enhancement network, which mitigates feature distortion by losslessly decomposing the input facial feature into high-frequency and low-frequency components using the wavelet transform and processing them separately. To improve the efficiency of facial feature extraction, a full domain Transformer is further proposed to enhance local, regional, and global low-frequency facial features. Such designs allow our method to perform better without stacking many network modules as previous methods did. Extensive experiments demonstrate that our method effectively balances performance, model size, and inference speed. All code and data will be released upon acceptance.
Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network
wenjie li Heng Guo Xuannan Liu Kongming Liang Jiani Hu Zhanyu Ma Jun Guo