Jingjing Li

Hyperspectral images (HSIs) are inevitably degraded by mixed noise, which hampers downstream interpretation. Recent graph-signal-processing denoisers encode the spatial–spectral structure of an HSI through a product graph over superpixel bodies, yet the adopted Kronecker (tensor) product graph retains only the joint spatial–spectral edges and discards the pure-spatial and pure-spectral edges—the two priors that govern HSI smoothness. We introduce a two-parameter weighted product-graph family that contains the Kronecker, Cartesian and strong products as exact special cases, and propose Weighted Strong Product Graph Laplacian Regularization (WSPGLR)—the strong-product branch with a tunable joint-edge weight β—embedded in a global low-rank plus sparse model solved by the Alternating Direction Method of Multipliers (ADMM) with singular-value-thresholding and soft-thresholding updates and a sparse conjugate-gradient (CG) solve. On three simulated cubes (Washington DC Mall, Pavia University, Indian Pines) under four mixed-noise scenarios, WSPGLR consistently improves Mean Peak Signal-to-Noise Ratio (MPSNR) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse) over a matched Kronecker-product control in every tested case (and Mean Structural Similarity (MSSIM) in 11 of 12 settings)—up to 2.5 dB in MPSNR from the graph term alone—and an ablation shows that β governs a spatial–spectral fidelity trade-off (Spectral Angle Mapper, SAM). On the detailed urban scenes, WSPGLR attains the highest MPSNR against the external methods and matched control in seven of eight settings, whereas it trails LRTDTV on the smooth agricultural scene; with an optional scene-adaptive TV step it attains the best average rank across scene types; a Tucker-based variant further shows the low-rank block is modular and generally improves spectral fidelity. Tests on four no-reference real HSIs and 30 paired real-noise MEHSI samples extend the sensor coverage; on MEHSI, the native-domain RND framework remains substantially stronger, which delimits the scope of the proposed training-free regularizer.

Roadside perception systems, also known as roadside units (RSUs), are critical in Vehicle-to-Everything (V2X) applications, yet spatio-temporal asynchrony between multiple sensors severely compromises the accuracy of fusion. In this paper, a spatio-temporal synchronization method for millimeter-wave (MMW) radar and camera fusion is proposed, integrating target matching based on dynamic time warping (DTW) with spatio-temporal parameter estimation. Leveraging the advantages of DTW in time-series alignment to calculate the similarity between radar and visual trajectories enables target matching and parameter estimation in sparse scenes. This method was validated on a real-world dataset containing over 30 pedestrian trajectories, covering scenarios with varying densities ranging from one to six pedestrians. The results indicate a temporal offset of 0.116 s between the camera and radar. Following synchronization, the average spatial deviation decreased from 1.4358 to 0.1074 m in the x-direction (i.e., across the road) and from 3.0732 to 0.1775 m in the y-direction (i.e., along the road). Consequently, this method provides an efficient solution for deploying roadside perception systems in sparse traffic environments.

Original high-definition radar data contains rich environmental information, including distance, Doppler velocity, and azimuth. However, extracting robust features from such sparse and noisy frequency-domain data remains a challenge. To address this issue, this paper proposes an improved multi-task network, the Efficient Feature Aggregation with Balanced Attention Radar Network (EFA-RadNet). This network introduces the VoVNetV2 architecture into the field of raw radar perception and effectively preserves feature diversity across different receptive fields through a One-Shot Aggregation (OSA) module, avoiding signal aliasing. In addition, we propose an attention mechanism module, Balanced effective Squeeze–Excitation (B-eSE), which is better suited for sparse radar processing and effectively addresses the problem of weak target loss in the radar spectrum. Experiments on the RADIal dataset show that our EFA-RadNet achieves excellent target detection performance while also attaining optimal accuracy in free space segmentation.