SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing

Due to the limitation of the focal length of the optical lens and the resolution of the infrared detector, distant closely-spaced infrared small target (CSIST) groups typically appear as mixing spots in the infrared image. In this article, we propose a novel task, sequential CSIST unmixing, namely, detecting all targets in the form of subpixel localization from a highly dense CSIST group. However, achieving such precise detection is an extremely difficult challenge. In addition, the lack of high-quality public datasets has also restricted the research progress. To this end, first, we contribute an open-source ecosystem, including SeqCSIST, a sequential benchmark dataset, and a toolkit that provides objective evaluation metrics for this special task, along with the implementation of 23 relevant methods. Furthermore, we propose the deformable refinement network (DeRefNet), a model-driven deep learning framework that introduces a temporal deformable feature alignment (TDFA) module, enabling adaptive interframe information aggregation. To the best of our knowledge, this work is the first endeavor to address the CSIST unmixing task within a multiframe paradigm. Experiments on the SeqCSIST dataset demonstrate that our method outperforms the state-of-the-art approaches with mean Average Precision (mAP) metric improved by 5.3%. Our dataset and toolkit are available at https://github.com/GrokCV/SeqCSIST