Search Results (312)

Synthetic Aperture Radar (SAR) enables high-resolution sea-surface wind speed retrieval. However, the enhanced spatial resolution of SAR imagery introduces substantial challenges, from small-scale contamination sources that significantly degrade retrieval accuracy. Particularly in coastal regions, non-wind-related backscatter signals, such as ships and oil slicks, can severely bias wind speed estimates at sub-kilometer scales. In this study, the first-order moment (average, m₁) and second-order moment (variance, m₂) are computed from the normalized radar cross-section (NRCS) within sub-images of Sentinel-1 SAR data acquired in Interferometric Wide (IW) mode. Analysis reveals that clean-sea-surface signals in both VV and VH polarizations cluster around an approximately linear empirical trend, m₂ = 2m₁ + b, in the m₁-m₂ statistical feature space, whereas the examined contamination types deviate from this trend and occupy separable regions. Based on this characteristic, a quality-control framework is proposed for the systematic separation of clean sea surface from image noise (border noise and inter-swath stripe noise) and non-ocean targets (land contamination, bright targets, and dark spots). Validation using independent SAR data from the Taiwan Strait was conducted separately for native 10 m and height-adjusted 3 m buoy observations. For the native 10 m observations, the RMSE and MBE were essentially unchanged at 1.5 m/s and −0.3 m/s, respectively. For the height-adjusted nearshore observations, the RMSE decreased from 3.2 m/s to 2.1 m/s and the MBE changed from −1.5 m/s to −1.1 m/s. Full article

Existing zero-shot low-light image enhancement methods often underutilize image priors, leading to noise amplification and color distortion. To address these issues, we propose a zero-shot framework for low-light image enhancement. The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, which is then used to guide diffusion-based enhancement with a pre-trained backbone. Specifically, the denoising module uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions. We then guide the reverse sampling of the pre-trained diffusion model with a refinement strategy operating in both the frequency and spatial domains, so that illumination enhancement and local detail refinement can be jointly achieved during sampling. At each step, Fourier-based reconstruction contributes to illumination enhancement while preserving structural information, and illumination-guided spatial adjustment further refines local brightness. Experiments on multiple benchmark datasets show that the proposed method improves illumination while preserving structural details. Full article

To address the performance degradation of UAV object detection under low-light conditions, we develop an end-to-end object detection network. This proposed method integrates contrastive learning into the detection framework and establishes feature consistency constraints between low-light and normal-light images through a hierarchical contrastive selection encoder. Since the encoder is required only during training and removed during inference, the proposed framework improves feature robustness without introducing additional inference cost. To further improve object detection accuracy, Frequency Guided Dynamic Attention (FGDA) is introduced into the object detection network, focusing on resolving the issue of redundant interference during feature transmission and enhancing feature representation capability. To improve multi-level spatial feature fusion, an Adaptive Gated Dual-Spatial Fusion (AGDSF) module is further developed, which adaptively strengthens target-relevant responses while weakening background noise. According to the experiments on the VisDrone (dark) dataset and a self-collected nighttime UAV-dark dataset illustrate that the proposed method ensures heightened detection accuracy with low computational overhead, complying with the real-time and robustness requirements of UAV perception in low-light contexts. Full article

Real-world low-light scenarios are complex, and annotated data is scarce. Meanwhile, existing supervised and mainstream unsupervised low-light image enhancement methods typically rely on large-scale paired labeled or unpaired normal-light data for training, which constrains the cross-scene generalization capability of these models. Furthermore, zero-shot low-light enhancement methods based on Retinex theory still exhibit notable performance shortcomings in dark-region noise suppression and illumination component estimation accuracy. To address these challenges, this paper proposes a zero-shot architecture for low-light image enhancement based on dual-domain synergistic downsampling and dual-branch feature calibration, which effectively resolves the core dilemma of the inaccessibility of annotated training data. Specifically, we construct a dual-domain downsampling mechanism with frequency-domain and wavelet complementarity, which provides effective priors for pre-denoising to suppress noise. A dual-branch feature calibration module centered on bidirectional correction and gated weighting is designed to achieve high-fidelity halo-free illumination estimation. To tackle the problems of color distortion and insufficient enhancement in extremely dark scenes, we further propose a multi-dimensional constrained naturalization enhancement module. Extensive experiments on the LOL-v1 and LOL-v2 datasets demonstrate that the proposed method achieves outstanding real low-light enhancement performance and competitive visual results. Full article

This study addresses the need for dark-current control and stable current response in planar high-purity germanium (HPGe) detectors for low-energy X-ray detection. A device fabrication strategy based on the coupled optimization of near-surface treatment, N/P junction formation, and guard-ring electrode design is proposed. Unlike previous studies that mainly focused on contact-layer fabrication, segmented electrode structures, low-noise readout, or response simulation, this work investigates low-damage near-surface construction, N-type and P-type contact-layer formation, and edge-related leakage-current regulation as an interconnected processing route. The relationship among the near-surface state, junction quality, electrode configuration, and edge-related leakage current is emphasized. Chemical mechanical polishing (CMP) reduced the surface roughness Sa of the HPGe crystal to 6.68 nm, providing a low-damage near-surface foundation for subsequent junction fabrication. On this basis, the optimized Li thermal diffusion process, namely 0.5 Å s⁻¹, 325 °C, and 5 min, formed an N-type contact layer with preserved lattice ordering and favorable electrical properties. B ion implantation combined with rapid thermal processing (RTP) achieved acceptor activation and implantation-damage recovery, and the condition with Rp = 198.1 nm showed relatively better structural recovery and electrical characteristics. After introducing the guard-ring electrode, the dark current of the device at −20 V decreased from 6.5 × 10⁻⁹ A to 2.03 × 10⁻⁹ A, and a stable switching current response was obtained under 12 keV monochromatic synchrotron X-ray irradiation. Geant4 simulations were further used as an auxiliary analysis to evaluate the effect of the guard-ring structure on the simulated response spectra and full-energy peak efficiency (FEPE) for low-energy X-rays. Overall, this study provides experimental evidence for process optimization of planar HPGe detectors with low dark current and stable low-energy current response. Full article

Style transfer involves applying features from a stylized image to a content image, which has proven useful for image coloring. Significant progress has been made in utilizing neural networks for unsupervised sketch coloring using style transfer; however, existing models typically require user guidance. In this paper, we propose a GAN-Enhanced Style Transfer Network for Anime Sketch Colorization (STNGAN) that can fully automate coloring without user supervision. STNGAN incorporates a self-attention mechanism that enables the generator to capture global details and enhance color saturation and richness. The discriminator employs DenseNet to strengthen feature propagation and improve training stability. Additionally, we introduced a sketch reconstruction loss function to mitigate coloring overflow. Edge extraction was applied to obtain quantitative metrics. By contrasting light and dark areas between different color blocks and comparing them with the original sketches, we could objectively evaluate experimental performance using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics. Ablation experiments were conducted to assess the impact of self-attention and DenseNet on coloring. The results indicate that the proposed method achieved consistent improvements over the selected baselines under the evaluated anime sketch colorization setting. Quantitative experiments showed that STNGAN achieved a PSNR of 10.560, an SSIM of 0.768, and an inception score (IS) of 1.755. Compared with the strongest competing method, pix2pixHD, STNGAN improved the PSNR by 6.8%, the SSIM by 1.7%, and the IS by 8.0%. A user study with 50 participants further confirmed the perceptual advantage of STNGAN, which obtained the highest mean opinion score (MOS) of 4.129. Full article

The ability to detect individual photons with exquisite temporal precision has transformed single-photon avalanche diodes (SPADs) into indispensable tools across photonics, with quantum communication emerging as one of their most demanding frontiers. In recent years, device engineering breakthroughs, including refined junction geometries, advanced avalanche quenching schemes and scalable array integration, have redefined the limits of SPAD performance. Parallel advances in fabrication precision and CMOS-compatible architectures have not only expanded spectral sensitivity from the visible to the near-infrared but also enabled systematic suppression of dark count rates, afterpulsing, and timing jitter. These developments have directly impacted the feasibility and robustness of quantum key distribution and time-correlated single-photon counting, where detection efficiency and noise suppression determine system fidelity. This review unifies recent progress in SPAD technology, weaving together innovations in device design, fabrication strategies, and parameter optimization to reveal their collective influence on next-generation quantum-enabled photonic systems. Looking forward, the convergence of hybrid material platforms, on-chip photonic–electronic co-integration, and intelligent quenching control is poised to elevate SPAD performance to meet, and potentially exceed, the stringent requirements of future quantum communication infrastructures. Full article

Visible light positioning (VLP) based on received signal strength (RSS) offers a low-cost solution for indoor localization, being easily implemented in a warehouse based on existing infrastructure. However, RSS-based VLP remains challenging in 3D and yields subpar performance compared to 2D due to the larger localization space, as well as the presence of dark spots where many LEDs are not bright enough. This limits the practical use cases of RSS-based VLP in industrial applications. We study the performance of RSS-based VLP on a 3D simulated environment by training various machine learning models, including Gaussian processes and Kolmogorov–Arnold networks on different representations of RSS data. Our findings show that the use of Gaussian processes for predicting distances to LEDs coupled with a logarithmic transformation and multilateration leads to both high-accuracy and high-precision predictions under thermal noise (p95 localization error of 10 cm under 50 dB SNR). With this technique, RSS-based VLP reaches levels of accuracy in our simulated 3D environment that are comparable to those reported for 2D applications, supporting the extension of RSS-based VLP to height-varying industrial use cases. Full article

Object detection in low-light scenes is difficult because weak illumination reduces local contrast, amplifies sensor noise, and makes small or occluded objects hard to localize. Existing enhancement-before-detection pipelines can improve visual brightness, but they are not always optimized for detection features, while transformer-style global reasoning is often too costly for lightweight detectors. To address this gap, we propose GE-Detection, a detector-side framework that integrates Global Sub-Sampled Attention (GSA), Efficient Multi-scale Attention (EMA), and dropout regularization into YOLO- and PicoDet-style architectures. GSA introduces lower-cost global context modeling through spatially reduced key-value tokens, EMA refines multi-scale fused features without aggressive channel compression, and dropout improves training-time regularization with no inference-time parameter overhead. Experiments on COCO, ExDark, BDD100K-Night, and NightOwls show that the method is most effective in low-light detection: on ExDark with YOLO11n, mAP50-95 improves from 34.39% to 36.74%, mAP50 from 56.24% to 59.27%, and Box (P) from 67.63% to 71.36%. The full YOLO11n variant uses 2.91M parameters and maintains 134.7 FPS on an RTX 2080 Ti under the tested setting. Cross-dataset and corruption experiments further indicate that the proposed modules improve localization under several nighttime domain shifts while retaining known limitations under severe noise and adverse weather. These results indicate that combining efficient global attention, multi-scale feature recalibration, and targeted regularization can improve low-light localization while keeping the detector practical for deployment. Full article

Single-photon Light Detection and Ranging (LiDAR), which is capable of detecting single-photon signals, has developed rapidly in the field of long-range imaging. Due to the long detection range and limited laser power, the accumulated signal photons of single-photon LiDAR are extremely sparse. Meanwhile, the dark current counts, backscattering noise, and background noise of the single-photon detector are significant, resulting in an extremely low signal-background ratio of the detection data. However, existing algorithms struggle to accomplish the depth reconstruction on data with extremely low signal-to-background ratio (SBR). To address the challenges of complex spatiotemporal correlation and feature sparsity in long-range single-photon imaging depth reconstruction, we design a deep reconstruction algorithm based on a classification formulation, specifically tailored for single-echo detection scenarios. We propose a wavelet denoising preprocessing module and a four-dimensional attention module to learn the spatiotemporal correlations of the photon-counting cube data. Sawtooth-arranged dilated convolutions are utilized during the pixel-wise denoising process to extract sparse features, and non-local total variation regularization combined with cross-entropy is introduced as a joint loss function. For depth reconstruction of data with an SBR of 1:100, the root-mean-square error is less than 0.022 m, which is 66.72% lower than that of the best baseline algorithm. It also achieves promising depth reconstruction results on data with an SBR of 1:300. Full article

To address the poor detection performance in low-light underground coal mine belt conveyors caused by information entropy degradation and high background noise, as well as the difficulty in multi-scale target extraction due to uneven entropy distribution, this paper proposes an efficient foreign object detection model named SIRI-YOLO based on an improved YOLOv11n architecture. First, a Self-Calibrating Illumination Network (SCINet) is introduced to restore image information entropy and enhance low-light adaptability. Second, the C2PSA module is enhanced to C2PSA-IRMB by incorporating an Inverted Residual Mobile Block (IRMB), improving multi-scale feature utilization and reducing ineffective entropy increase. Third, an improved Reparameterized Generalized Feature Pyramid Network (RepGFPN) is adopted to strengthen the fusion of high-level semantics and low-level spatial features, reducing information entropy loss during feature pyramid transfer. Finally, the Inner-MPDIoU loss function is introduced to replace CIoU, achieving more accurate entropy minimization from a KL divergence perspective. Experimental results on a dataset containing large coal chunks and anchor rods show that SIRI-YOLO achieves 92.8% mAP@50, 59.4% mAP@50:95, 89.5% precision, and 87.2% recall, with only 2.92M parameters and 70.01 FPS, outperforming mainstream YOLO models. Furthermore, on the public ExDark low-light dataset, SIRI-YOLO improves mAP@50 by 4.2% over YOLOv11n, demonstrating strong generalization across different low-light and complex scenarios. The proposed method effectively handles uneven illumination, scale variation, and complex backgrounds, offering a practical solution for coal mine safety through system entropy reduction. Full article

The field of infrared (IR) photodetection is undergoing rapid development through the emergence of solution-processable nanoparticle (NP)-based materials and fabrication strategies. This review critically examines recent advances in fabrication approaches for NP-based IR detectors, emphasizing the relationship between synthesis, surface engineering, deposition processes, and device architecture in determining detector performance. Representative material platforms are discussed, including colloidal quantum dots (CQDs) such as PbS and HgTe, which enable tunable operation from the near-infrared (NIR) and short-wave infrared (SWIR) to selected mid-wave (MWIR), long-wave (LWIR), and emerging very-long-wave infrared (VLWIR) regimes depending on material composition and operating conditions. Further platforms including plasmonic metal NPs, black phosphorus, and topological nanomaterials are evaluated for their unique mechanisms of optical enhancement and broadband response. Fabrication approaches including continuous-flow synthesis, ligand exchange, blade coating, inkjet printing, electrophoretic deposition, and other scalable solution-processing methods are analyzed with respect to their influence on film quality, charge transport, interface engineering, and integration compatibility. The review further compares major device architectures, including photoconductors, photodiodes, plasmonic absorbers, and phototransistors, using key performance metrics such as specific detectivity (D*), responsivity (R), response speed, and operating temperature, while emphasizing the importance of measurement conditions in cross-platform comparisons. Critical challenges including dark-current generation, 1/f noise, transport limitations associated with ligand chemistry, environmental instability of narrow-bandgap materials, manufacturability constraints, and toxicity considerations are also discussed. Emerging directions such as neuromorphic sensing, CMOS-compatible integration, and sustainable lead-free nanomaterials are highlighted. By linking nanoscale material design and fabrication processes to device-level performance, this review provides a framework for advancing NP-based IR technologies toward scalable and application-relevant sensing systems. Full article

This paper presents a dual-axis sun sensor employing a cross-slit aperture in conjunction with a four-quadrant trapezoidal photodiode layout. The cross-slit configuration enhances angular sensitivity and resolution, while the trapezoidal photodiode geometry preserves a high signal-to-noise ratio at both near-normal incidence and large Sun angles, maintaining reliable directional discriminability around normal incidence. Compared with conventional quad-triangle photodiode layouts, the proposed trapezoidal geometry avoids the rapid collapse of the illuminated area near the triangular apex at large incidence angles, thereby preserving signal margin near the field-of-view boundary. System-level optical verification demonstrates that, after calibration, the proposed sensor achieves an angular accuracy of ±0.3° (3$\sigma$). To mitigate performance variations induced by temperature drift, an embedded shielded dummy photodiode is incorporated to provide a dark-current reference for compensation. Unlike compensation approaches that mainly rely on pre-characterization or offline calibration, the embedded shielded dummy photodiode provides an in situ, real-time dark-current reference for compensating for temperature-induced signal drift in the actual operating environment. Experimental results under dark conditions indicate that the embedded dummy photodiode served as a dark-current reference for compensating the temperature-dependent dark-current variation in the active photodiodes, reducing the peak-to-peak dark-signal variation by 96% over a temperature range from 20 °C to 120 °C. Furthermore, a pyramid-type sun-sensor architecture is proposed by integrating the dual-axis fine sun sensor with four wide-field coarse sun sensors. This system-level configuration extends the effective Sun field of view from the conventional 120°–180° range to approximately 280°, enabling near-hemispherical Sun-angle observability for enhanced attitude determination robustness. Full article

Crop leaf disease segmentation in complex natural environments remains challenging because lesion regions often exhibit substantial scale variation, blurred boundaries, and severe background interference. To address these issues, this study proposes a Multi-Scale Feature Rectification Network (MFR-Net) for crop leaf disease segmentation. The proposed network adopts an EfficientNetV2-S-based encoder to extract hierarchical features, incorporates a hybrid attention mechanism to enhance lesion-sensitive spatial and channel representations, introduces a Cross-Window Atrous Spatial Pyramid Pooling (CWASPP) module to strengthen multi-scale contextual modeling, and employs a Feature Rectification Module (FRM) in the decoder to alleviate semantic inconsistency during cross-level feature fusion. Experiments on a Kaggle-derived benchmark constructed from the unaugmented data folder of the public Leaf Disease Segmentation Dataset, containing 588 diseased-leaf images and 588 corresponding binary lesion masks, showed that MFR-Net achieved the highest mIoU of 74.27% and the highest Recall of 87.61% among the compared methods, and maintained competitive Dice performance (84.25%) with 25.10 M parameters and 37.55 G FLOPs. Ablation results further confirmed the effectiveness of the proposed design, with CWASPP providing the most notable individual contribution. Additional experiments were conducted on an independent Apple Leaf Dataset comprising 3197 image–mask pairs, collected under mixed controlled and natural field-like imaging conditions. The results showed competitive performance under a different data distribution, and robustness evaluation further verified stable performance under severe noise, blur, darkness, and contrast variation. All experiments were implemented in PyTorch 2.11.0 (CUDA 12.8) on a workstation equipped with an NVIDIA GeForce RTX 4060 Ti GPU (8 GB). These results indicate that MFR-Net provides an effective and robust solution for crop leaf disease segmentation in complex agricultural scenarios. Full article

Under complex lighting conditions, particularly in low-light environments, general object detectors often suffer from degraded detection performance due to insufficient brightness, severe noise, and loss of discriminative details. This issue is especially critical in underground mining scenarios, where weak illumination, complex backgrounds, dust interference, and frequent small or partially occluded targets make reliable visual perception highly challenging. To address this issue, we propose an Illumination-Aware Detection Network (IADNet) for object detection in low-light environments. Specifically, an Illumination Modeling Subnetwork (IMS) is designed to extract illumination-aware and degradation-aware auxiliary features from low-light images. Within the IMS, an Adaptive Weighted Downsampling (AWD) layer is introduced to reduce noise interference during feature downsampling and enhance illumination-aware representation learning. Furthermore, a Global Feature Enhancement Module (GFEM) is incorporated to strengthen global context modeling and improve feature representation capability in complex scenes. In addition, an extra contrastive loss is introduced to constrain the optimization of the IMS, and weighting factors are employed to balance the detection loss and the contrastive loss during training. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of the proposed method. On the public ExDark dataset, IADNet achieves an mAP@50 of 80.3%, outperforming the baseline YOLO11m by 3.4 percentage points. On the self-constructed mining low-light dataset Lowlight_Mine, the proposed method achieves 92.3% Precision, 82.0% Recall, 89.3% mAP@50, and 57.8% mAP@50:95, showing favorable performance in object detection tasks under mining-related low-light scenarios. On the DARK FACE dataset, IADNet achieves 54.6% mAP@50 and 31.2% mAP@50:95, further indicating its robustness under real low-light conditions. On the synthetic low-light Dark_VOC dataset, IADNet attains an mAP@50 of 91.6%, and on the normal-light VOC dataset, it achieves an mAP@50 of 93.0%, suggesting that the proposed method maintains stable detection performance under the evaluated illumination conditions. These results indicate that IADNet improves low-light object detection performance and provides a useful experimental reference for object detection tasks in mining-related low-light scenarios. Full article