Abstract
With the increasing deployment of intelligent CCTV systems in outdoor environments, there is a growing demand for face recognition systems optimized for challenging weather conditions. Adverse weather significantly degrades image quality, which in turn reduces recognition accuracy. Although recent face image restoration (FIR) models based on generative adversarial networks (GANs) and diffusion models have shown progress, their performance remains limited due to the lack of dedicated modules that explicitly address weather-induced degradations. This leads to distorted facial textures and structures. To address these limitations, we propose a novel GAN-based blind FIR framework that integrates two key components: local Statistical Facial Feature Transformation (SFFT) and Degradation-Agnostic Feature Embedding (DAFE). The local SFFT module enhances facial structure and color fidelity by aligning the local statistical distributions of low-quality (LQ) facial regions with those of high-quality (HQ) counterparts. Complementarily, the DAFE module enables robust statistical facial feature extraction under adverse weather conditions by aligning LQ and HQ encoder representations, thereby making the restoration process adaptive to severe weather-induced degradations. Experimental results demonstrate that the proposed degradation-agnostic SFFT model outperforms existing state-of-the-art FIR methods based on GAN and diffusion models, particularly in suppressing texture distortions and accurately reconstructing facial structures. Furthermore, both the SFFT and DAFE modules are empirically validated in enhancing structural fidelity and perceptual quality in face restoration under challenging weather scenarios.
Abstract (translated)
随着智能CCTV系统在户外环境中的部署日益增加，对于能够在恶劣天气条件下优化的面部识别系统的市场需求也在不断增长。不良天气条件显著降低图像质量，进而减少了识别准确率。尽管基于生成对抗网络（GAN）和扩散模型的最新面部图像恢复（FIR）方法显示出了一定的进步，但由于缺乏专门针对由天气引起的退化问题进行处理的模块，其性能仍然有限，导致了面部纹理和结构变形。 为了克服这些限制，我们提出了一种新颖的基于GAN的盲态FIR框架，该框架集成了两个关键组件：局部统计面部特征转换（SFFT）以及不受退化影响的特征嵌入（DAFE）。局部SFFT模块通过使低质量（LQ）面部区域与高质量（HQ）对应部分之间的局部统计分布对齐来增强面部结构和颜色保真度。同时，DAFE模块允许在恶劣天气条件下进行鲁棒性统计面部特征提取，通过使LQ和HQ编码器表示对齐，从而使恢复过程适应于严重的由天气引起的退化。 实验结果表明，所提出的不受退化影响的SFFT模型优于现有的基于GAN和扩散模型的FIR方法，在抑制纹理失真并准确重建面部结构方面尤其有效。此外，无论是SFFT模块还是DAFE模块都在挑战性天气场景下的面部恢复过程中被实证验证为能够增强结构保真度和感知质量。
URL
https://arxiv.org/abs/2507.07464
