Background: X-ray computed tomography (CT) imaging technology provides high-precision anatomical visualization of patients and has become a standard modality in clinical diagnostics. A widely adopted strategy to mitigate radiation exposure is sparse-view scanning. However, traditional iterative approaches require manual design of regularization priors and laborious parameter tuning, while deep learning methods either heavily depend on large datasets or fail to capture global image correlations. Methods: Therefore, this paper proposes a combination of model-driven and data-driven methods, using the ADMM iterative algorithm framework to constrain the network to reduce its dependence on data samples and introducing the CNN and Transformer model to increase the ability to learn the global and local representation of images, further improving the accuracy of the reconstructed image. Results: The quantitative and qualitative results show the effectiveness of our method for sparse-view reconstruction compared with the current most advanced reconstruction algorithms, achieving a PSNR of 42.036 dB, SSIM of 0.979, and MAE of 0.011 at 32 views. Conclusions: The proposed algorithm has effective capability in sparse-view CT reconstruction. Compared with other deep learning algorithms, the proposed algorithm has better generalization and higher reconstruction accuracy.

In clinical medical applications, sparse-view computed tomography (CT) imaging is an effective method for reducing radiation doses. The iterative reconstruction method is usually adopted for sparse-view CT. In the process of optimizing the iterative model, the approach of directly solving the quadratic penalty function of the objective function can be expected to perform poorly. Compared with the direct solution method, the alternating direction method of multipliers (ADMM) algorithm can avoid the ill-posed problem associated with the quadratic penalty function. However, the regularization items, sparsity transform, and parameters in the traditional ADMM iterative model need to be manually adjusted. In this paper, we propose a data-driven ADMM reconstruction method that can automatically optimize the above terms that are difficult to choose within an iterative framework. The main contribution of this paper is that a modified U-net represents the sparse transformation, and the prior information and related parameters are automatically trained by the network. Based on a comparison with other state-of-the-art reconstruction algorithms, the qualitative and quantitative results show the effectiveness of our method for sparse-view CT image reconstruction. The experimental results show that the proposed method performs well in streak artifact elimination and detail structure preservation. The proposed network can deal with a wide range of noise levels and has exceptional performance in low-dose reconstruction tasks.

Monitoring the burning surface regression in ground static ignition tests is crucial for predicting the internal ballistic performance of solid rocket motors (SRMs). A previously proposed ultra-sparse computed tomography imaging method provides a possibility for real-time monitoring. However, sample shortages of SRMs highlights the need for monitoring accuracy, especially given the high cost associated with the design and development of SRM systems. Therefore, constructing datasets via regression simulations to compensate for SRM sample shortages is critical. To address this issue, we recommend adopting the level-set method to dynamically track the burning surface by solving partial differential equations (PDEs). The computational cost of numerical solution is prohibitive for scientific applications involving large-scale spatiotemporal domains. The physics-informed neural network (PINN) and neural operator have been used to accelerate the solution of PDE, showing satisfactory prediction performance and high computational efficiency. We designed a physics-guided network, named LS-PhyNet, that couples the potential physical mechanisms of burning surface regression into the deep learning framework. The proposed method is capable of encoding well-established traditional numerical discretization methods into the network architecture to leverage prior knowledge of underlying physics, thus providing the model with enhanced expressive power and interpretability. Experimental results prove that LS-PhyNet can better reproduce the burning surfaces obtained by numerical solution with only small data regimes, providing a new paradigm for real-time monitoring of burning surface regression transients during static ignition tests.

Low-altitude Unmanned Aerial Vehicle (UAV) detection using LiDAR range images faces persistent challenges. These include sparse features for long-range targets, large scale variations caused by viewpoint changes, and severe interference from complex backgrounds. To address these issues, we propose an improved detection framework based on YOLOv10. First, we design a Swin-Conv hybrid module that combines sparse attention with deformable convolution. This module enables the network to focus on informative regions and adapt to target geometry. These capabilities jointly strengthen feature extraction for sparse, long-range targets. Second, we introduce Attentional Feature Fusion (AFF) in the neck to replace naïve feature concatenation. AFF employs multi-scale channel attention to softly select and adaptively weight features from different levels, improving robustness to multi-scale targets. In addition, we systematically study how the viewpoint distribution in the training set affects performance. The results show that moderately increasing the proportion of low-elevation-view samples significantly improves detection accuracy. Experiments on a self-built simulated LiDAR range-image dataset demonstrate that our method achieves 88.96% mAP at 54.2 FPS, which is 4.78 percentage points higher than the baseline. Deployment on the Jetson Orin Nano edge device further validates the model’s potential for real-time applications. The proposed method remains robust under noise and complex backgrounds. The proposed approach achieves an effective balance between detection accuracy and computational efficiency, providing a reliable solution for real-time target detection in complex low-altitude environments.

To improve depth reconstruction accuracy of streak tube imaging LiDAR (STIL) under weak echo and low-contrast conditions in complex scattering environments, this paper proposes a hierarchical reliability-guided multispectral polarization reconstruction framework (MSP-STIL). The proposed method addresses measurement uncertainty in multi-wavelength and multi-polarization observations by constructing a progressive reliability modeling strategy, which evolves from polarization stability to statistical uncertainty and finally to signal strength enhancement. First, a Dual-channel Polarization Contrast (Pc) is introduced to evaluate local scattering stability and suppress fringe peak degradation caused by polarization distortion. Second, SNR is employed to model the uncertainty of depth measurements across different wavelength channels. Finally, echo intensity is incorporated as a confidence refinement factor to further enhance high-quality signals. Based on this hierarchical modeling strategy, an adaptive inverse-variance weighting scheme is developed to achieve robust multi-wavelength depth fusion. The results show that the proposed method outperforms equal-weight and single-feature methods in all test regions. Compared with the non-weighted method, the MAE, RE, MSE, RMSE, and STD are reduced by approximately 10.95%, 12.02%, 25.44%, 13.68%, and 15.58% on average, respectively. Under low signal-to-noise conditions (simulated by controlled noise levels) and long-distance detection scenarios, the proposed method still maintains low reconstruction errors and effectively suppresses depth fluctuations, demonstrating good noise resistance and distance robustness. In addition, the maximum contrast of the RGB image constructed through weighted fusion increases from 5.0065 to 5.8300, verifying the effectiveness of the proposed method in low-contrast complex scenes. Overall, the proposed MSP-STIL multi-feature joint weighting method shows clear advantages in reconstruction accuracy, robustness, and scene adaptability.

To address the challenges of insufficient feature representation and the difficulty of detecting sparse and distant objects in UAV-borne LiDAR point clouds—which exhibit significantly lower point density than terrestrial/mobile LiDAR scans—this paper proposes an enhanced detection algorithm built upon the PointPillars framework. First, a coordinate attention mechanism is incorporated to enhance the network’s ability to capture spatial geometric information. Furthermore, the backbone network is redesigned with a dual-path structure and a feature modulation fusion module, enabling adaptive integration of multi-scale features. Experimental evaluations conducted on a custom simulated UAV-borne LiDAR point cloud dataset demonstrate that the proposed method achieves 85.35% 3D mAP and 88.26% BEV mAP, corresponding to absolute improvements of 21.61 and 7.85 percentage points compared with the original PointPillars model. In addition, the proposed approach demonstrates consistent performance on the publicly available KITTI benchmark through preliminary cross-dataset validation. The results indicate that the proposed method can effectively improve the detection accuracy and robustness of LiDAR-based 3D object detection in complex environments.