Person Re-Identification Enhanced by Super-Resolution Technology

With rising demand for cross-camera person re-identification (ReID) in smart cities, low-resolution (LR) images severely hinder practical ReID performance due to detail loss and weakened identity features. This paper proposes two solutions to address this bottleneck: (1) super-resolution (SR) techniques, including hybrid attention transformer (HAT), pixel-level and semantic-level adjustable SR (PiSA-SR), and omni aggregation networks for lightweight image SR (Omni-SR), are used to enhance image visual quality, and the enhanced images are applied to three ReID methods, including semantically controllable self-supervised learning framework-REID (SOLIDER-REID), light-REID, and relation-aware global attention (RGA), for performance assessment. (2) An end-to-end framework integrating HAT and SOLIDER-REID is designed, in which HAT enhances LR images via multi-scale attention to restore discriminative details, while SOLIDER-REID’s semantic controller suppresses background noise to focus on the pedestrian regions. Extensive experiments on the Market-1501 dataset show that the first solution slightly improves ReID accuracy, e.g., PiSA-SR + SOLIDER-REID achieves 92.0% mAP, 0.4% higher than SOLIDER-REID alone, while slightly sacrificing speed. The second solution significantly boosts LR ReID performance at the cost of a certain increase in time. For LR images, even 32 × 32 images, HAT-SOLIDER achieves 59.8% mAP and 80.4% Rank-1, 18.5% higher in mAP and 19.2% higher in Rank-1 than SOLIDER-REID alone. This work provides effective solutions for LR-induced performance degradation in cross-camera ReID.