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arXiv · 2506.14121

FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution

Siyu Xu·Wenjie Li·Guangwei Gao·Jian Yang·Guo-Jun Qi·Chia-Wen Lin
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Abstract

Face super-resolution (FSR) under limited computational budgets remains challenging. Existing methods often treat all facial pixels equally, leading to suboptimal resource allocation and degraded performance. CNNs are sensitive to high-frequency facial features such as contours and outlines, while Mamba excels at capturing low-frequency attributes like facial color and texture with lower complexity than Transformers. Motivated by this, we propose FADPNet, a Frequency-Aware Dual-Path Network that decomposes facial features into low- and high-frequency components for dedicated processing. The low-frequency branch employs a Mamba-based Low-Frequency Enhancement Block (LFEB) that integrates state-space attention with squeeze-and-excitation to restore global interactions and emphasize informative channels. The high-frequency branch uses a CNN-based Depthwise Position-aware Attention (DPA) module to refine structural details, followed by a lightweight High-Frequency Refinement (HFR) module for further frequency-specific refinement. These designs enable FADPNet to achieve a strong balance between FSR quality and efficiency, outperforming existing methods. Codes will be available at https://github.com/IVIPLab/FADPNet.

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Siyu Xu, Wenjie Li, Guangwei Gao, Jian Yang, Guo-Jun Qi, Chia-Wen Lin. 2025-06-17. FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution. https://arxiv.org/abs/2506.14121

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