  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
feature into high 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 facial features. Such designs allow our method to perform
better without stacking many modules as previous methods did. Experiments show
that our method effectively balances performance, model size, and speed. Code
link: https://github.com/PRIS-CV/WFEN.
