In low-light optical imaging, the scarcity of incident photons and the inherent limitations of imaging sensors lead to challenges such as low signal-to-noise ratio, limited dynamic range, and degraded contrast, severely compromising image quality and optical information integrity. To address these challenges, we propose a novel low-light image enhancement technique, LightenResDiff, which combines a residual diffusion model with the discrete wavelet transform. The core innovation of LightenResDiff lies in it accurately restoring the low-frequency components of an image through the residual diffusion model, effectively capturing and reconstructing its fundamental structure, contours, and global features. Additionally, the dual cross-coefficients recovery module (DCRM) is designed to process high-frequency components, enhancing fine details and local contrast. Moreover, the perturbation compensation module (PCM) mitigates noise sources specific to low-light optical environments, such as dark current noise and readout noise, significantly improving overall image fidelity. Experimental results across four widely-used benchmark datasets demonstrate that LightenResDiff outperforms existing methods both qualitatively and quantitatively, surpassing the current state-of-the-art techniques.

The widespread application of UAV technology has brought significant security concerns that cannot be ignored, driving considerable attention to anti-unmanned aerial vehicle (UAV) tracking technologies. Anti-UAV tracking faces challenges, including target entry into and exit from the field of view, thermal crossover, and interference from similar objects, where Siamese network trackers exhibit notable limitations in anti-UAV tracking. To address these issues, we propose FSTC-DiMP, an anti-UAV tracking algorithm. To better handle feature extraction in low-Signal-to-Clutter-Ratio (SCR) images and expand receptive fields, we introduce the Large Selective Kernel (LSK) attention mechanism, achieving a balance between local feature focus and global information integration. A spatio-temporal consistency-guided re-detection mechanism is designed to mitigate tracking failures caused by target entry into and exit from the field of view or similar-object interference through spatio-temporal relationship analysis. Additionally, a background augmentation module has been developed to more efficiently utilise initial frame information, effectively capturing the semantic features of both targets and their surrounding environments. Experimental results on the AntiUAV410 and AntiUAV600 datasets demonstrate that FSTC-DiMP achieves significant performance improvements in anti-UAV tracking tasks, validating the algorithm’s strong robustness and adaptability to complex environments.

Polarization in addition to wavelength, amplitude and phase is a fundamental property of light that provides additional multidimensional information for detecting and identifying targets with different materials and surface states. It offers advantages for target recognition under complex conditions. However, due to limitations in polarization imaging mechanisms, existing spectral polarization systems are often bulky and unsuitable for deployment on small unmanned aerial vehicles (UAVs), which restricts their broader application. To evaluate the feasibility of UAV-based spectral polarization detection, this study designs a spectral polarization camera based on a division-of-aperture architecture. The system integrates six spectral bands (450 nm, 555 nm, 660 nm, 720 nm, 750 nm, and 840 nm) and four polarization angles (0°, 45°, 90°, and 135°). Calibration experiments show that the average relative radiance error across spectral channels is below 4.92%, the maximum absolute deviation of the optical axis is less than 1.57°, and all polarization extinction ratios exceed 1000. Based on this system, UAV-based field experiments were conducted to analyze same-color targets. The results indicate that effective binary segmentation can be achieved by exploiting differences in the degree of polarization, with better target discrimination than visible light data. These findings validate the practicality of the proposed method.

While the rapid development and widespread application of drone technology have brought about significant advancements, they have also introduced security challenges, making anti-UAV technology a key research focus. However, existing methods still face severe challenges when dealing with UAV tracking in complex scenarios. To address this, this paper proposes an integrated Motion-associated Detection and Tracking Collaboration (MDTC) system for anti-UAV applications. To better handle the perception of target existence states, we designed a motion association module that dynamically senses the presence of targets and responds quickly to target disappearance. Simultaneously, to address the issue of feature degradation in small targets, we optimized the detection branch to enhance robust perception of multi-scale targets. Additionally, the proposed verification matching mechanism can infer the integrity and reliability of targets in occluded scenarios, ensuring stable tracking. Compared to existing methods, our approach achieves superior performance across three benchmark datasets. On Anti-UAV600, it attains IoU, ACC, and SR scores of 0.525, 0.427, and 0.641, respectively—surpassing the second-best method, GlobalTrack, by 6.2%, 6.4%, and 5.9%. These gains highlight the method’s strengths in prompt target response, scale adaptability, and occlusion awareness, underscoring its reliability and practicality for real-world deployment.