Unsupervised Image Sequence Registration and Enhancement for Infrared Small Target Detection.

In the burgeoning realm of deep learning and with the introduction of the infrared target detection dataset, infrared small target detection has increasingly garnered attention. Within this domain, multi-frame infrared small target detection stands as a pivotal and challenging sub-task. Notably, some recent methods have exhibited commendable performance in multi-frame infrared scenes. However, these methods were tethered to time-consuming background alignment pre-processing, which impedes their real-world application of multi-frame infrared target detection systems. In this paper, an unsupervised end-to-end framework tailored for infrared image sequence registration was proposed. This framework diverges from traditional registration methods by incorporating a novel Basket-based Hierarchical Temporal Consistency loss. The proposed loss function achieve intra-basket consistency and inter-basket diversity, effectively mitigating issues related to inconsistency. Additionally, the framework includes the Input Thresholding Mask and Output Transformation Mask. These components are crucial for guiding the network’s training process and correcting misalignments. Moreover, the introduction of a dual-level residual enhancer is proposed to enhance the quality of registered images, thereby improving overall performance. Extensive experimental results have demonstrated the superiority of the proposed method over baseline methods. The proposed method achieved a significant improvement in the F ₁ - score metric on a public dataset, reaching 0.8882, and an inference speed of 23.34 FPS. This represents an improvement of 0.0190 in performance and a sixfold increase in speed compared to the state-of-the-art method in multi-frame infrared small target detection.