AI 中文总结
该研究提出基于Transformer的协作特征聚合方法与级联SR网络，从多序列或多视角人脸图像实现人脸超分辨率，其统一的身份表示可提升行人重识别性能，效果优于现有方法。
AI 中文摘要
我们提出了一种新颖的协作方法，用于从序列或多视角人脸图像中实现人脸超分辨率（SR）和鲁棒行人重识别。传统SR方法在从低质量低分辨率图像恢复人脸时，常出现模糊和失真问题；基于图像和视频的人脸SR方法，若使用人脸关键点或分割技术，也存在类似挑战。为克服这些局限，我们利用跨时间或视角的多个相关人脸观测，引入一种基于Transformer的协作特征聚合方法，该方法统一多序列或多视角数据中的身份特征，使同一人的多序列人脸能共同助力准确估计通用人脸特征。此外，我们提出一种级联SR网络，通过逐步统一人脸特征来渐进恢复目标人脸的高分辨率图像。统一后的身份表示可进一步用于行人重识别场景，即使在图像严重退化的情况下也能实现准确匹配。详尽的实验结果与对比表明，我们的方法优于其他最先进方法，在人脸超分辨率和重识别性能上均展现出持续提升。本研究凸显了从多个人脸输入联合进行身份重建和渐进式图像恢复，对增强下游视觉识别任务的有效性。
英文摘要
We propose a novel collaborative approach for face super-resolution (SR) and robust person re-identification from sequential or multi-view facial images. Traditional SR methods often suffer from blurring and distortion in faces recovered from poor-quality images due to low resolution. Image- and video-based facial SR methods using facial landmarks or segmentation also have similar challenges. To overcome these limitations, we leverage multiple correlated facial observations, across time or viewpoints, by introducing a transformer-based collaborative feature aggregation method that unifies identity features from multi-sequence or multi-view data. This allows faces in multiple sequences of an individual to contribute to accurately estimating common facial features. Furthermore, we propose a cascade SR network to progressively restore the high-resolution image of the target's face with gradual facial feature unification. The unified identity representation is further utilized in person re-identification scenarios, enabling accurate matching even under severe image degradation. The exhaustive experimental results and comparisons show that our method outperforms other state-of-the-art methods, demonstrating consistent improvements in both face super-resolution and re-identification performance. Our work highlights the effectiveness of joint identity reconstruction and progressive image restoration from multiple facial inputs in enhancing downstream visual recognition tasks.
Journal refMultimedia Systems, vol. 31, no. 5, pp. 341, Aug. 2025
DOI：10.1007/s00530-025-01918-y
