Infrared Small Target Detection via Interpatch Correlation Enhancement and Joint Local Visual Saliency Prior

Small target detection is the primary technology for infrared search and tracking (IRST) systems and plays a vital role in practical applications. Existing algorithms have the following challenges: 1) insufficient local and nonlocal feature extraction and 2) imbalance between accuracy and real-time detection performance. In this study, a novel model for fast detection based on interpatch correlation enhancement (IPCE) and joint local visual saliency prior is proposed to overcome such issues. Regarding the correlation in interpatch dimension, the improved tensor nuclear norm is used to further extract the low-rank structure of the background tensor, which fully exploits the low-rank component and reduces the iteration times. Furthermore, with the hypothesis that the target is locally saliency, a prior model based on the visual saliency mechanism is proposed as the constraint of the target tensor. It effectively reduces the false detection of the sparse edge structure. In general, the proposed IPCE jointly exploits both local and nonlocal correlation of the original image, achieving robustness in different scenarios. Finally, the proposed model is solved by the alternating direction method of multipliers (ADMM). Experiments on seven datasets demonstrate that IPCE outperforms the state of the arts in terms of the balance between detection efficiency and accuracy.