Search Results (100)

Images obtained from imaging instruments can endure issues such as high degradation, color distortion, and weak brightness. Effective systems for enhancing these images are critically required. To improve the image quality, herein, we propose two filters based on simple functions, including cosine, sine, hyperbolic secant, and the inverse of hyperbolic cosecant. These filters are used for enhancing the images obtained from a fluorescence imaging system, a near-infrared camera, and low-light condition. The contrast is increased while the image quality is improved. They perform better than a matched filter. Moreover, the combination of our filters with the filter based on the watershed algorithm or the matched filter can be used to extract the marginal features from images generated under water environment. Furthermore, their application in image fusion is explored. Our designed filters may be potentially used for future applications on target identification and tracking. Full article

Multimodal object detection utilizing RGB and infrared (IR) imagery has become a critical research area for unmanned aerial vehicle (UAV) surveillance applications, providing reliable perception under various lighting and environmental conditions. Nevertheless, current methods encounter three primary challenges: (1) insufficient utilization of frequency-domain properties in heterogeneous modalities, (2) restricted adaptability in crossmodal feature integration across different environmental scenarios, and (3) inadequate modeling of fine-grained spatial relationships for accurate object localization. To overcome these limitations, we introduce MFE-DETR, a novel Multimodal Feature-Enhanced Detection Transformer that achieves superior RGB-IR fusion through three complementary innovations. First, we present the Dual-Modality Enhancement Module (DMEM) with two specialized processing streams: the Haar wavelet decomposition stream (HWD-Stream) that conducts multi-resolution frequency-domain analysis to independently enhance low-frequency structural components and high-frequency textural information, and the Attention-guided Kolmogorov–Arnold Refinement Stream (AKR-Stream) that employs learnable spline-parameterized activation functions for adaptive nonlinear feature refinement. Second, we enhance the Cross-scale Channel Feature Fusion module by integrating an Adaptive Feature Fusion Module (AFAM) with complementary gating mechanisms that dynamically adjust modality contributions according to spatial informativeness. Third, we introduce the Bilinear Attention-Enhanced Detection Module (BADM) that models second-order feature interactions through factorized bilinear pooling, facilitating fine-grained crossmodal correlation analysis. Extensive experiments on the DroneVehicle benchmark show that MFE-DETR attains 78.6% ${\mathrm{mAP}}_{50}$ and 57.8% ${\mathrm{mAP}}_{50:95}$, outperforming state-of-the-art approaches by 5.3% and 3.7%, respectively. Additional evaluations on the VisDrone dataset further confirm the excellent generalization performance of our method, especially for small object detection with 18.6% ${\mathrm{AP}}_S$, achieving a 1.5% improvement over existing techniques. Comprehensive ablation studies and visualizations offer detailed insights into the effectiveness of each proposed component. Full article

Object recognition in remote sensing images is essential for applications such as land resource monitoring, maritime vessel detection, and emergency disaster assessment. However, detection accuracy is often limited by complex backgrounds, densely distributed targets, and multiscale variations. To address these challenges, this study aims to improve the detection of small-scale and densely distributed objects in complex remote sensing scenes. An improved object detection network is proposed, called omnidirectional and adaptive YOLOv8 (OA-YOLOv8), based on the YOLOv8 architecture. Two targeted enhancements are introduced. First, an omnidirectional perception refinement (OPR) network is embedded into the backbone to strengthen multiscale feature representation through the incorporation of receptive-field convolution with a triplet attention mechanism. Second, an adaptive channel dynamic upsampling (ACDU) module is designed by combining DySample, the Haar wavelet transform, and a self-supervised equivariant attention mechanism (SEAM) to dynamically optimize channel information and preserve fine-grained features during upsampling. Experiments on the satellite imagery multi-vehicle dataset (SIMD) demonstrate that OA-YOLOv8 outperforms the original YOLOv8 by 4.6%, 6.7%, and 4.1% in terms of mAP@0.5, precision, and recall, respectively. Visualization results further confirm its superior performance in detecting small and dense targets, indicating strong potential for practical remote sensing applications. Full article

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions. Full article

This technical note presents a quantitative image-based framework for evaluating the packing and compressibility of granular soils, specifically applied to building foundation design in civil infrastructure projects. The Sedimaging system replicates hydraulic sedimentation in a controlled column, equipped with a high-resolution camera, to visualize particle orientation after deposition. Grayscale images of the settled bed are analyzed using Haar Wavelet Transform (HWT) decomposition to quantify directional intensity gradients. A new descriptor, termed the sediment index (B), is defined as the ratio of vertical to horizontal wavelet energy at the dominant scale, representing the preferential alignment and anisotropy of particles during sedimentation. Experimental investigations were conducted on fifteen granular materials that include natural sands, tailings, glass beads and rice grains with different shapes. The results demonstrate strong correlations between B and both microscopic shape ratios (d₁/d₂ and d₁/d₃) and macroscopic properties. Linear relationships predict the limiting void ratios (e_max, e_min) with mean absolute differences of 0.04 and 0.03, respectively. A power-law function relates B to the compression index (C_c) with an average deviation of 0.02. These findings confirm that the sediment index effectively captures the morphological influence of particle shape on soil packing and compressibility. Compared with conventional physical testing, the Sedimaging-based approach offers a rapid, non-destructive, and high-throughput solution for estimating soil packing and compressibility of cohesionless, sand-sized granular soils directly from post-settlement imagery, making it particularly valuable for preliminary site assessments, geotechnical screening, and intelligent monitoring of granular materials in building foundation design and other infrastructure applications, such as earth–rock dams. Full article

Remote sensing acquires Earth surface information without physical contact through sensors operating at diverse spatial, spectral, and temporal resolutions. In high-resolution remote sensing imagery, objects often exhibit large scale variation, complex spatial distributions, and strong inter-class similarity, posing persistent challenges for accurate semantic segmentation. Existing methods still struggle to simultaneously preserve fine boundary details and model long-range spatial dependencies, and lack explicit mechanisms to decouple low-frequency semantic context from high-frequency structural information. To address these limitations, we propose the Multi-Perspective Information Fusion Network (MPIFNet) for remote sensing semantic segmentation, motivated by the need to integrate global context, local structures, and multi-frequency information into a unified framework. MPIFNet employs a Global and Local Mamba Block Self-Attention (GLMBSA) module to capture long-range dependencies while preserving local details, and a Double-Branch Haar Wavelet Transform (DBHWT) module to separate and enhance low- and high-frequency features. By fusing spatial, hierarchical, and frequency representations, MPIFNet learns more discriminative and robust features. Evaluations on the Vaihingen, Potsdam, and LoveDA datasets through ablation and comparative studies highlight the strong generalization of our model, yielding mIoU results of 86.03%, 88.36%, and 55.76%. Full article

Urban scene classification in high-resolution remote sensing images is critical for applications such as power facility site selection and grid security monitoring. However, the complexity and variability of ground objects present significant challenges to accurate classification. While convolutional neural networks (CNNs) excel at extracting local features, they often struggle to model long-range dependencies. Transformers can capture global context but incur high computational costs. To address these limitations, this paper proposes a Global–Local Information Fusion Network (GLIFNet), which integrates VMamba for efficient global modeling with CNN for local detail extraction, enabling more effective fusion of fine-grained and semantic information. Furthermore, a Haar Wavelet Transform Attention Mechanism (HWTAM) is designed to explicitly exploit frequency-domain characteristics, facilitating refined fusion of multi-scale features. The experiment compared nine commonly used or most advanced methods. The results show that GLIFNet achieves mean F1 scores (mF1) of 90.08% and 87.44% on the ISPRS Potsdam and ISPRS Vaihingen datasets, respectively. This represents improvements of 1.26% and 1.91%, respectively, compared to the compared model. The overall accuracy (OA) reaches 90.43% and 92.87%, with respective gains of 2.28% and 1.58%. Experimental results on the LandCover.ai dataset demonstrate that GLIFNet achieved an mF1 score of 88.39% and an accuracy of 92.23%, exhibiting relative improvements of 0.3% and 0.28% compared with the control model. In summary, GLIFNet demonstrates advanced performance in urban scene classification from high-resolution remote sensing images and can provide accurate basic data for power construction. Full article

Quantum watermarking is a technique that embeds specific information into a quantum carrier for the purpose of digital copyright protection. In this paper, we propose a novel color image watermarking algorithm that integrates quantum discrete wavelet transform with Sinusoidal–Tent mapping and baker mapping. Initially, chaotic sequences are generated using Sinusoidal–Tent mapping to determine the channels suitable for watermark embedding. Subsequently, a one-level quantum Haar wavelet transform is applied to the selected channel to decompose the image. The watermarked image is then scrambled via discrete baker mapping, and the scrambled image is embedded into the High-High subbands. The invisibility of the watermark is evaluated by calculating the peak signal-to-noise ratio, Structural similarity index measure, and Learned Perceptual Image Patch Similarity, with comparisons made against the color histogram. The robustness of the proposed algorithm is assessed through the calculation of Normalized Cross-Correlation. In the simulation results, PSNR is close to 63, SSIM is close to 1, LPIPS is close to 0.001, and NCC is close to 0.97. This indicates that the proposed watermarking algorithm exhibits excellent visual quality and a robust capability to withstand various attacks. Additionally, through ablation study, the contribution of each technique to overall performance was systematically evaluated. Full article

Aviation electrification is an inevitable trend leading the development of future aviation technology, and its development cannot be separated from high-performance onboard power systems. As a key equipment of the system, the DC converter plays a core role in energy conversion, and its operational reliability directly affects the stability of the entire system. As the core component of the converter, capacitors have become a weak link in system reliability due to their high failure rate. Therefore, accurate monitoring of their health status is of great significance. To achieve fast, high-precision online monitoring of capacitors, this paper proposes an intelligent monitoring strategy that integrates Haar wavelet transform and Kalman filter. This method only requires the collection of inductance current and output voltage signals during regular operation, without the need for additional installation sensors. The capacitance current is reconstructed and used to accurately identify the capacitance value (C) and equivalent series resistance (ESR) throughout the entire life cycle in strong noise environments. The simulation and experimental results show that the strategy has good robustness under different operating conditions, with recognition errors of C and ESR controlled within 3% and 2%, respectively, demonstrating the feasibility of the proposed method. Full article

Quantum communication has achieved significant performance gains compared to classical systems but remains sensitive to channel noise and decoherence. These limitations become especially critical in quantum image transmission, where high-dimensional visual data must be preserved with both structural fidelity and robustness. In this context, transform-based quantum encoding methods have emerged as promising approaches, yet their relative performance under noisy conditions has not been fully explored. This paper presents a comparative study of two such methods, the quantum Fourier transform (QFT) and the quantum Haar wavelet transform (QHWT), within an image transmission framework. The process begins with source coding (JPEG/HEIF), followed by channel coding to enhance error resilience. The bitstreams are then mapped into quantum states using variable qubit encoding and transformed using either QFT or QHWT prior to transmission over noisy quantum channels. At the receiver, the corresponding decoding operations are applied to reconstruct the images. Simulation results demonstrate that the QFT achieves superior performance under noisy conditions, consistently delivering higher Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Universal Quality Index (UQI) values across different qubit sizes and image formats compared to the QHWT. This advantage arises because QFT uniformly spreads information across all basis states, making it more resilient to noise. By contrast, QHWT generates localized coefficients that capture structural details effectively but become highly vulnerable when dominant coefficients are corrupted. Consequently, while QHWT emphasizes structural fidelity, QFT provides superior robustness, underscoring a fundamental trade-off in quantum image communication. Full article

This study proposes ToRLNet, a lightweight tomato ripeness detector designed for real-time deployment in resource-constrained agricultural settings. Built on YOLOv12n, ToRLNet integrates three self-constructed modules (WaveFusionNet for frequency–spatial enhancement and feature extraction, ETomS for efficient context-aware encoding, and SFAConv for selective multi-scale downsampling) to address subtle inter-stage color transitions, small fruit instances, and cluttered canopies. We benchmark ToRLNet against lightweight and small-scale YOLO baselines (YOLOv8–YOLOv12) and conduct controlled ablations isolating each module’s contribution. ToRLNet attains Precision 90.27%, Recall 86.77%, F1-score 88.49%, mAP50 91.76%, and mAP 78.01% with only 6.9 GFLOPs, outperforming representative nano/small YOLO variants under comparable compute budgets. Ablation results show WaveFusionNet improves spectral–textural robustness, ETomS balances the precision–recall trade-off while reducing redundancy, and SFAConv preserves fine chromatic gradients and boundary structure during downsampling; their combination yields the most balanced performance. These findings demonstrate that ToRLNet delivers a favorable accuracy–efficiency trade-off and provides a practical foundation for on-board perception in automated harvesting, yield estimation, and greenhouse management. Full article

Aiming at the challenges faced by the detection of small-scale ship targets in Synthetic Aperture Radar (SAR) images, this paper proposes a novel deep learning network named FAWT-Net based on attention-matrix despeckling and Haar wavelet reconstruction. This network collaboratively optimizes the detection performance through three core modules. First, during the feature transfer stage from backbone to the neck, a filtering module based on attention matrix is designed, which can suppress the speckle noise. Then, during feature upsampling stage, a wavelet transform feature upsampling method for reconstructing image details is designed to enhance the distinguishability of target boundaries and textures. At the same time, the network also combines sub-image feature stitching downsampling to avoid losing key details in small targets, and adopts a scale-sensitive detection head. By adaptively adjusting the shape constraints of prediction boxes, it effectively solves the regression deviation problem of ship targets with inconsistent aspect ratios. Verified by experiments on SSDD and LS-SSDD, the proposed method improves AP50 by 1.3% and APS by 0.8% on the SSDD. Meanwhile, it is verified that the proposed method has higher precision and recall rates on the LS-SSDD, and the recall rate has been increased by 2.2%. Full article

This paper presents a modified Orthogonal Frequency Division Multiplexing (OFDM) system that combines Discrete Wavelet Transform (DWT) with Discrete Sine Transform (DST) to enhance data rate capacity over traditional Discrete Fourier Transform (DFT)-based OFDM systems. By applying Inverse Discrete Wavelet Transform (IDWT) to the modulated Binary Phase Shift Keying (BPSK) bits, the constellation diagram reveals that half of the time-domain samples after single-level Haar IDWT are zeros, while the other half are real. The proposed system utilizes these $0.5N$ zero values, modulating them with the DST (IDST) and assigning them as the imaginary part of the signal. Performance comparisons demonstrate that the Bit-Error-Rate (BER) of this hybrid DWT-DST configuration lies between that of BPSK and Quadrature Phase Shift Keying (QPSK) in a DWT-based system, while also achieving data rate improvement of $0.5N$. Additionally, simulation results indicate that the proposed approach demonstrates stable performance even in the presence of estimation errors, with less than 3.4% BER degradation for moderate errors, and consistently better robustness than QPSK-based systems while offering improved data rate efficiency over BPSK. This novel configuration highlights the potential for more efficient and reliable data transmission in OFDM systems, making it a promising alternative to conventional DWT or DFT-based methods. Full article

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier. Full article

Underwater biological object detection plays a critical role in intelligent ocean monitoring and underwater robotic perception systems. However, challenges such as image blurring, complex lighting conditions, and significant variations in object scale severely limit the performance of mainstream detection algorithms like the YOLO series and Transformer-based models. Although these methods offer real-time inference, they often suffer from unstable accuracy, slow convergence, and insufficient small object detection in underwater environments. To address these challenges, we propose FPH-DEIM, a lightweight underwater object detection algorithm based on an improved DEIM framework. It integrates three tailored modules for perception enhancement and efficiency optimization: a Fine-grained Channel Attention (FCA) mechanism that dynamically balances global and local channel responses to suppress background noise and enhance target features; a Partial Convolution (PConv) operator that reduces redundant computation while maintaining semantic fidelity; and a Haar Wavelet Downsampling (HWDown) module that preserves high-frequency spatial information critical for detecting small underwater organisms. Extensive experiments on the URPC 2021 dataset show that FPH-DEIM achieves a mAP@0.5 of 89.4%, outperforming DEIM (86.2%), YOLOv5-n (86.1%), YOLOv8-n (86.2%), and YOLOv10-n (84.6%) by 3.2–4.8 percentage points. Furthermore, FPH-DEIM significantly reduces the number of model parameters to 7.2 M and the computational complexity to 7.1 GFLOPs, offering reductions of over 13% in parameters and 5% in FLOPs compared to DEIM, and outperforming YOLO models by margins exceeding 2 M parameters and 14.5 GFLOPs in some cases. These results demonstrate that FPH-DEIM achieves an excellent balance between detection accuracy and lightweight deployment, making it well-suited for practical use in real-world underwater environments. Full article