## 4 Conclusions  
This article presents a sparse representation-based model for infrared polarization super-resolution reconstruction. We propose a method to overcome the limitations of the detector's sampling on the focal plane and achieve satisfactory results without requiring a large training set. Consequently, this approach is suitable for tasks where obtaining a vast amount of data is not feasible. Specifically, instead of independently solving each polarization angle, we solve the model on a designed Stokes vector field. This strategy effectively avoids errors caused by differential calculations during the polarization image-solving process. The simulation results depicted in Figure 3 demonstrate that our method effectively suppresses partial pseudo-polarization phenomena and generates more realistic polarization information compared to other existing methods. Furthermore, the results from a real test scene illustrated in Figure 5 reveal the significant advantages of our reconstruction results in object detection and classification tasks. Our algorithm delivers clearer edges of the targets and a noticeable contrast between the target and background, marking an improvement over the other algorithms discussed in this paper. In addition, to construct the dictionary set, we employ a self-developed detector to create a near-real-time short-wave infrared time-sharing polarization system. This system is equipped with a high-precision grating disk, enabling the continuous acquisition of polarization images. The main findings of our research are described in this article as follows:  
(1) We propose a super-resolution reconstruction model for infrared polarization. (2) We design a reconstruction method based on the model. (3) We produce a prototype of a near-real-time short-wave infrared time-sharing polarization system and collect an infrared polarization dataset. (4) We build a short-wave infrared focal plane polarization system and conduct experiments to demonstrate the excellent results achieved by the proposed method in terms of quantitative measurement and visual quality. In our future research, we intend to optimize the algorithms mentioned in the paper to enhance their speed in a software environment, with a primary focus on accelerating the feature symbol search algorithm. Moreover, we plan to integrate the proposed algorithm into our developed short-wave focal plane polarization detection system. Additionally, our future work involves incorporating convex optimization algorithms and deep learning methods to further improve image quality. Furthermore, we aim to explore the combination of metasurface design and its application in achieving full optical image reconstruction. Methodology, Y.M. ; software, Y.M. and T.L. ; validation, Y.M., S.W. and Z.Y. ; formal analysis, S.W. ; resources, F.L., S.W. and W.Q. ; data curation, Y.M. and L.L. ; writing--original draft preparation, Y.M. ; writing--review and editing, F.L. ; project administration, F.L. and L.L. ; funding acquisition, F.L. and W.Q. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Pre-research Program during the 14 th Five-Year Plan, grant number No. 514010405. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. **Data Availability Statement:** Our data are derived from a self-developed time-sharing and focal-plane short-wave polarized infrared system. The system has been thoroughly refined and is now available for purchase. It encompasses imaging equipment, acquisition cards, an industrial computer, and software. Due to the inclusion of sensitive information, the dataset is not publicly accessible. Please reach out to the first author for the source code if necessary. **Conflicts of Interest:** The authors declare no conflicts of interest.