Estimation of Fractal Dimension and Super-Resolution Reconstruction for Person Re-Identification in Images from Infrared Surveillance Camera

Person re-identification (Re-ID) using infrared surveillance cameras has attracted increasing attention due to its robustness under low-light conditions. However, infrared images generally suffer from a low spatial resolution, which degrades Re-ID performance. To address this issue, this study proposes a part attention and contrastive loss-based super-resolution reconstruction network (PCSR-Net) and a unified infrared-only Re-ID framework. The proposed PCSR-Net consists of a correlation-based super-resolution reconstruction network (CoSR-Net), a feature extractor for Re-ID, and a part attention mechanism that estimates the importance of different body regions. In addition, contrastive loss and part-aware reconstruction loss are incorporated to guide the super-resolution process toward identity-discriminative representations. Experimental results on DBPerson-Recog-DB1 and SYSU-MM01 demonstrate that the proposed method outperforms state-of-the-art approaches in terms of the equal error rate (EER), mean average precision (mAP), and rank-1 accuracy, validating its effectiveness for infrared-based person Re-ID.