Computer tomography (CT) technology is widely used in non-destructive testing. To improve detection speed, fewer sampling angles are usually used, but this poses a challenge to the accuracy of image reconstruction. Deep learning is currently the latest technology in the field of fault imaging, but due to the lack of emphasis on reconstructing detailed information, the performance of existing methods is poor. Therefore, this article proposes a dual-view projection 3D CT reconstruction network based on the two-stage fusion of details and structure. This method utilizes DR projection images from two perspectives to achieve reconstruction while restoring high-quality details. Specifically, we will divide the reconstruction into two steps. Firstly, we utilize encoder-decoder architecture and cross feature map attention enhancement operator to achieve large-scale information recovery, thereby reconstructing device framework and other information; Secondly, introducing differential convolution to reconstruct small-scale detail information achieves the effect of detail enhancement. Finally, the two-step features are fused to output the 3D reconstructed volume. The algorithm also proposes an advanced loss variant that adds projection loss to common losses to improve reconstruction accuracy. To verify the effectiveness of the algorithm, we conducted validation on an open database named Chips_P_3D that we built ourselves. At the same time, to verify the generalization ability, we also conducted reconstruction experiments on fused datasets with complex information. The experiment shows that the reconstruction quality has been significantly improved.

High-density stacked semiconductor devices exhibit complex three-dimensional structures in industrial CT imaging, leading to insufficient defect localization accuracy when relying solely on single-slice 2D detection. To address this challenge, we propose a multi-class defect detection framework that integrates a feature-guided wavelet module (T-Wavelet) and a cross-dimensional attention fusion module (CDAF). The T-Wavelet module enhances subtle defect patterns through high-frequency amplification and low-frequency suppression while reducing background redundancy, providing structural priors for inter-slice feature integration. The CDAF module explicitly models spatial continuity among adjacent slices within a local depth range and maps 3D volumetric information into 2D channel representations, enabling unified 2D-3D feature modeling. Their combination forms the TW-CDAF module, which further strengthens local defect sensitivity and compensates for cross-layer structural discontinuities, thereby endowing a 2D detector with 3D structural perception. We construct a CT dataset of high-density stacked semiconductor devices covering multiple defect types. Experimental results demonstrate that the proposed framework achieves superior performance over mainstream detectors in key metrics such as mAP, validating its high accuracy and robustness under complex device structures.

Background and objective Sparse-view computed tomography (CT) speeds up scanning and reduces radiation exposure in medical diagnosis. However, when the projection views are severely under-sampled, deep learning-based reconstruction methods often suffer from over-smoothing of the reconstructed images due to the lack of high-frequency information. To address this issue, we introduce frequency domain information into the popular projection-image domain reconstruction, proposing a Tri-Domain sparse-view CT reconstruction model based on Sparse Transformer (TD-STrans). Methods TD-STrans integrates three essential modules: the projection recovery module completes the sparse-view projection, the Fourier domain filling module mitigates artifacts and over-smoothing by filling in missing high-frequency details; the image refinement module further enhances and preserves image details. Additionally, a multi-domain joint loss function is designed to simultaneously enhance the reconstruction quality in the projection domain, image domain, and frequency domain, thereby further improving the preservation of image details. Results The results of simulation experiments on the lymph node dataset and real experiments on the walnut dataset consistently demonstrate the effectiveness of TD-STrans in artifact removal, suppression of over-smoothing, and preservation of structural fidelity. Conclusion The reconstruction results of TD-STrans indicate that sparse transformer across multiple domains can alleviate over-smoothing and detail loss caused by reduced views, offering a novel solution for ultra-sparse-view CT imaging.

Multi-view X-ray imaging systems extend the imaging field of view by integrating multiple imaging modules. Nevertheless, inter-module performance variations in detector arrays, gain drift, and angle-dependent effects cause cross-view grayscale inconsistencies, which degrade overall imaging quality. While existing X-ray imaging approaches primarily rely on single-view flat-field correction or per-projection normalization, these strategies remain inadequate for achieving global consistency across views. To overcome this limitation, this study proposes a physics-constrained multi-view image correction (PCMIC) method. By constructing a globally shared attenuation field to model view-specific nonlinear responses and incorporating boundary anchoring together with monotonicity constraints, the method ensures strong physical fidelity. Experiments conducted on a self-developed large-field, high-resolution micro-CT system demonstrate that PCMIC effectively suppresses stitching artifacts and improves grayscale consistency, structural fidelity, and quantitative stability.

Micro-CT imaging is widely used in materials science, biomedical research, geological exploration, and industrial inspection due to its non-destructive capabilities. However, traditional systems face challenges due to limitations in spatial bandwidth product (SBP), making it difficult to achieve both a large field-of-view (FOV) and high-resolution imaging simultaneously. This paper presents a segmented light deflection method using cascaded optical fiber tapers integrated within a "fiber taper-image intensifier-camera" coupling architecture. This design reduces distortion caused by large taper ratios in single-stage systems. Additionally, a multi-view geometric registration calibration approach is developed based on CT projection geometric constraints to address mismatches between traditional 2D planar distortion correction methods and CT projection geometry. A cascaded fiber taper array (53 mm input - 34 mm intermediate - 16 mm output) was implemented in a 2 × 2 tiling configuration. The system achieves wide-FOV micro-CT imaging with a maximum imaging area of 106.4 × 106.4 mm2. Resolution testing confirmed a 3 μm resolution within a 4.1 × 4.1 mm2 FOV. System reconstruction efficacy was validated through rock core and printed circuit board (PCB) samples.

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.

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.

Computed tomography (CT) has evolved into an indispensable tool for clinical diagnosis. Reducing radiation dose crucially minimizes adverse effects but may introduce noise and artifacts in reconstructed images, affecting diagnostic processes for physicians. Scholars have tackled deep learning training instability by exploring diffusion models. Given the scarcity of clinical data, we propose the unsupervised image domain score generation model (UISG) for low-dose CT reconstruction. During training, normal-dose CT images are utilized as network inputs to train a score-based generative model that captures the prior distribution of CT images. In the iterative reconstruction, the initial CT image is obtained using a filtered back-projection algorithm. Subsequently, diffusion-based prior, high-frequency convolutional sparse coding prior, and data-consistency steps are employed to obtain the high-quality reconstructed image. Given the global characteristics of noise, the score network of the diffusion model utilizes a swin transformer structure to enhance the model’s ability to capture long-range dependencies. Furthermore, convolutional sparse coding is applied exclusively to the high-frequency components of the image, to prevent over-smoothing or the loss of crucial anatomical details during the denoising process. Quantitative and qualitative results indicate that UISG outperforms competing methods in terms of denoising and generalization performance.

Multiple-object tracking (MOT) is a crucial component in autonomous driving systems. However, inaccurate object detection is always the bottleneck for MOT. Most detectors are not designed to take the temporal information across consecutive frames into consideration. To take advantage of such information, we design a novel data representation, the spatio-temporal (ST) map, which collects a batch of detection results spatio-temporally, and we train a novel network, ST-TrackNet, to assign predicted track IDs to each positive detection across a sequence. With our ST map detection fed into the tracker, the correlation of objects between adjacent frames becomes prominent, which improves the performance of the tracker in the data association step. Moreover, the long-term trajectory in a sequence also helps to refine the detection results. We train and evaluate our network on the KITTI dataset, a CARLA simulation dataset, and a dataset recorded in a factory environment. Our approach generally achieves superior performance over the state-of-the-art. Note to Practitioners —We investigate the MOT problem in this paper. A spatio-temporal pipeline is proposed to provide a solution to this problem. Object detection results produced by off-the-shelf object detectors are used to form the proposed ST maps. In low signal-to-noise ratio (SNR) situations, our proposed framework can achieve more accurate and robust tracking results with more false-positives. Due to the simplicity and modular design of our framework, it can be applied directly after the detection stage to achieve the online tracking task. The proposed method is evaluated on several datasets, and the experimental results demonstrate its effectiveness. Our method can also be used for other autonomous driving applications, such as path planning and trajectory prediction.

This paper employs five different general-purpose optimization methods to perform parameter optimization on single-layer hollow antiresonant fibers. It provides guidance on the establishment of hyperparameters for various optimization methods, with the aim of further defining and standardizing the necessary conditions and convergence criteria for applying optimization algorithms to specialty optical fibers. Through numerical experiments, the study ultimately obtains the converged optimal performance and the range of optimized parameter guidance for single-layer, double-layer, and triple-layer antiresonant fibers with different topological structures.

Reconstructing computed tomography (CT) images from an extremely limited set of projections is crucial in practical applications. As the available projections significantly decrease, traditional reconstruction and model-based iterative reconstruction methods become constrained. This work aims to seek a reconstruction method applicable to fast CT imaging when available projections are highly sparse. To minimize the time and cost associated with projections acquisition, we propose a deep learning model, X-CTReNet, which parameterizes a nonlinear mapping function from orthogonal projections to CT volumes for 3D reconstruction. The proposed model demonstrates effective capability in inferring CT volumes from two-view projections compared to baseline methods, highlighting the significant potential for drastically reducing projection acquisition in fast CT imaging.

Mobile crowd sensing (MCS) is a popular sensing paradigm that recruits participants carrying sensing devices to collect data for specific tasks in a sensing area. In an MCS system, when a participant executes limited multiple location-based tasks according to different task accessing sequences, its executing time which depends on its travelling path is different. Therefore, how to perform an efficient path planning to find the optimal executing time for each participant is of vital importance. In this paper, we revisit the path planning problem in MCS and propose a novel path planning method based on the beetle swarm optimization (BSO) algorithm. First, a participant executing time is defined as the sum of the moving time along its travelling path and the sensing time for executing all tasks on this path. Then, to minimize it, an improved BSO algorithm is utilized to find the optimal path for each participant. In particular, a largest-order-value (LOV) rule is introduced to convert each position of beetles from a multi-dimensional vector into a travelling path for the participant. The simulation results verify that the proposed method is superior to other baselines in terms of the participant executing time and shows a faster convergence speed.

Objective.Sparse-view computed tomography (SVCT), which can reduce the radiation doses administered to patients and hasten data acquisition, has become an area of particular interest to researchers. Most existing deep learning-based image reconstruction methods are based on convolutional neural networks (CNNs). Due to the locality of convolution and continuous sampling operations, existing approaches cannot fully model global context feature dependencies, which makes the CNN-based approaches less efficient in modeling the computed tomography (CT) images with various structural information. Approach. To overcome the above challenges, this paper develops a novel multi-domain optimization network based on convolution and swin transformer (MDST). MDST uses swin transformer block as the main building block in both projection (residual) domain and image (residual) domain sub-networks, which models global and local features of the projections and reconstructed images. MDST consists of two modules for initial reconstruction and residual-assisted reconstruction, respectively. The sparse sinogram is first expanded in the initial reconstruction module with a projection domain sub-network. Then, the sparse-view artifacts are effectively suppressed by an image domain sub-network. Finally, the residual assisted reconstruction module to correct the inconsistency of the initial reconstruction, further preserving image details. Main results. Extensive experiments on CT lymph node datasets and real walnut datasets show that MDST can effectively alleviate the loss of fine details caused by information attenuation and improve the reconstruction quality of medical images. Significance. MDST network is robust and can effectively reconstruct images with different noise level projections. Different from the current prevalent CNN-based networks, MDST uses transformer as the main backbone, which proves the potential of transformer in SVCT reconstruction.

For autonomous driving systems, the storage cost and transmission speed of the large-scale point clouds become an important bottleneck because of their large volume. In this paper, we propose a range image-based three-stage framework to compress the scanning LiDAR's point clouds using the entropy model. In our three-stage framework, we refine the coarser range image by converting the regression problem into the limited classification problem to improve the performance of generating accurate point clouds. And in the feature extraction part, we propose a novel attention Conv layer to fuse the voxel-based 3D features in the 2D range image. Compared with the Octree-based compression methods, the range image compression with the entropy model performs better in the autonomous driving scene. Experiments on LiDARs with different lines and in different scenarios show that our proposed compression scheme outperforms the state-of-the-art approaches in reconstruction quality and downstream tasks by a wide margin.

In autonomous vehicles or robots, point clouds from LiDAR can provide accurate depth information of objects compared with 2D images, but they also suffer a large volume of data, which is inconvenient for data storage or transmission. In this paper, we propose a Range image-based Point Cloud Compression method, R-PCC, which can reconstruct the point cloud with uniform or non-uniform accuracy loss. We segment the original large-scale point cloud into small and compact regions for spatial redundancy and salient region classification. Our range image-based method can keep and align all points from the original point cloud in the reconstructed point cloud, and the setting of the quantization module restricts the maximum reconstruction error. In the experiments, we prove that our easier FPS-based segmentation method can achieve better performance than instance-based segmentation methods such as DBSCAN, and our non-uniform compression framework shows a great improvement on the downstream tasks compared with the state-of-the-art large-scale point cloud compression methods. Our real-time method can achieve 40 × compression ratio without affecting downstream tasks, to act as a baseline for range image-based point cloud compression. The code is available on https://github.com/StevenWang30/R-PCC.git.

Light detection and ranging (LiDAR) plays an indispensable role in autonomous driving technologies, such as localization, map building, navigation and object avoidance. However, due to the vast amount of data, transmission and storage could become an important bottleneck. In this article, we propose a novel compression architecture for multi-line LiDAR point cloud sequences based on clustering and convolutional long short-term memory (LSTM) networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to 2D array, represented as range images. Thus, we cast the 3D point clouds compression as a range image sequence compression problem. Inspired by the high efficiency video coding (HEVC) algorithm, we design a novel compression framework for LiDAR data that includes two main techniques: intra-prediction and inter-prediction. For intra-frames, inspired by the depth modeling modes (DMM) adopted in 3D-HEVC, we develop a clustering-based intra-prediction technique, which can utilize the spatial structure characteristics of point clouds to remove the spatial redundancy. For inter-frames, we design a prediction network model using convolutional LSTM cells. The network model is capable of predicting future inter-frames using the encoded intra-frames. As a result, temporal redundancy can be removed. Experiments on the KITTI dataset demonstrate that the proposed method achieves an impressive compression ratio (CR), with 4.10% at millimeter precision, which means the point clouds can compress to nearly 1/25 of their original size. Additionally, compared with the well-known octree, Google Draco, and MPEG TMC13 methods, our algorithm yields better performance in compression ratio.

We present a novel octree-based multi-level framework for large-scale point cloud compression, which can organize sparse and unstructured point clouds in a memory-efficient way. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree using the context of siblings' children, ancestors, and neighbors to encode the occupancy information of each non-leaf octree node into a bitstream. Moreover, we locally fit quadratic surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding. These strong priors empower our entropy framework to encode the octree into a more compact bitstream. In the decoding stage, we apply a two-step heuristic strategy to restore point clouds with better reconstruction quality. The quantitative evaluation shows that our method outperforms state-of-the-art baselines with a bitrate improvement of 11-16% and 12-14% on the KITTI Odometry and nuScenes datasets, respectively.

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.

Since early 2020, COVID-19 has spread rapidly across the world. At the date of this writing, the disease has been globally reported in 220 countries and regions, infected more than 68 million people, and caused more than 1.5 million deaths (see https://covid19.who.int/) for up-to-date statistics. Avoiding person-to-person transmission is an effective approach to control and prevent the pandemic. However, many daily activities, such as transporting goods in our daily life, inevitably involve person-to-person contact.