Search Results (257)

Quantum communication presents new opportunities for overcoming the limitations of classical wireless systems, particularly those associated with noise, fading, and interference. Building upon the principles of classical orthogonal frequency division multi-plexing (OFDM), this work proposes a quantum OFDM architecture tailored for video transmission. In the proposed system, video sequences are first compressed using the versatile video coding (VVC) standard with different group of pictures (GOP) sizes. Each GOP size is processed through a channel encoder and mapped to multi-qubit states with various qubit configurations. The quantum-encoded data is converted from serial-to-parallel form and passed through the quantum Fourier transform (QFT) to generate mutually orthogonal quantum subcarriers. Following reserialization, a cyclic prefix is appended to mitigate inter-symbol interference within the quantum channel. At the receiver, the cyclic prefix is removed, and the signal is restored to parallel before the inverse QFT (IQFT) recovers the original quantum subcarriers. Quantum decoding, classical channel decoding, and VVC reconstruction are then employed to recover the videos. Experimental evaluations across different GOP sizes and channel conditions demonstrate that quantum OFDM provides superior resilience to channel noise and improved perceptual quality compared to classical OFDM, achieving peak signal-to-noise ratio (PSNR) up to 47.60 dB, structural similarity index measure (SSIM) up to 0.9987, and video multi-method assessment fusion (VMAF) up to 96.40. Notably, the eight-qubit encoding scheme consistently achieves the highest SNR gains across all channels, underscoring the potential of quantum OFDM as a foundation for future high-quality video transmission. Full article

Ghost imaging (GI) offers a robust framework for remote sensing under degraded visibility conditions. However, atmospheric scattering in phenomena such as fog introduces significant noise and signal attenuation, thereby limiting its efficacy. Inspired by the selective attention mechanisms of biological visual systems, this study introduces a novel deep learning (DL) architecture that embeds a self-attention mechanism to enhance GI reconstruction in foggy environments. The proposed approach mimics neural processes by modeling both local and global dependencies within one-dimensional bucket measurements, enabling superior recovery of image details and structural coherence even at reduced sampling rates. Extensive simulations on the Modified National Institute of Standards and Technology (MNIST) and a custom Human-Horse dataset demonstrate that our bio-inspired model outperforms conventional GI and convolutional neural network-based methods. Specifically, it achieves Peak Signal-to-Noise Ratio (PSNR) values between 24.5–25.5$\mathrm{dB}/\mathrm{m}$ and Structural Similarity Index Measure (SSIM) values of approximately 0.8 under high scattering conditions ($\beta \ge 3.0 \mathrm{dB}/\mathrm{m}$) and moderate sampling ratios ($N \ge 50\%$). A comparative analysis confirms the critical role of the self-attention module, providing high-quality image reconstruction over baseline techniques. The model also maintains computational efficiency, with inference times under $0.12 \mathrm{s}$, supporting real-time applications. This work establishes a new benchmark for bio-inspired computational imaging, with significant potential for environmental monitoring, autonomous navigation and defense systems operating in adverse weather. Full article

Real-world image denoising faces a critical trade-off: Convolutional Neural Network (CNN)-based methods are computationally efficient but limited in capturing long-range dependencies, while Transformer-based approaches achieve superior global modeling at prohibitive computational costs (>100 G Multiply–Accumulate Operations, MACs). This presents significant challenges for deployment in resource-constrained environments. We present a practical CNN–Transformer hybrid network that systematically balances performance and efficiency under practical deployment constraints for real-world image denoising. By integrating key components from NAFNet (Nonlinear Activation Free Network) and Restormer, our method employs three design strategies: (1) strategic combination of CNN and Transformer blocks enabling performance–efficiency trade-offs; (2) elimination of nonlinear operations for hardware compatibility; and (3) architecture search under explicit resource constraints. Experimental results demonstrate competitive performance with significantly reduced computational cost: our models achieve 39.98–40.05 dB Peak Signal-to-Noise Ratio (PSNR) and 0.958–0.961 Structural Similarity Index Measure (SSIM) on the SIDD dataset, and 39.73–39.91 dB PSNR and 0.959–0.961 SSIM on the DND dataset, while requiring 7.18–16.02 M parameters and 20.44–44.49 G MACs. Cross-validation results show robust generalization without significant performance degradation across diverse scenes, demonstrating a favorable trade-off among performance, efficiency, and practicality. 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

This paper proposes a quantum multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) framework for image transmission, which combines quantum multi-qubit encoding with spatial and frequency diversity to enhance noise resilience and image quality. The system utilizes joint photographic experts group (JPEG), high efficiency image file format (HEIF), and uncompressed images, which are first source-encoded (if applicable) and then processed using classical channel encoding. The channel-encoded bitstream is mapped into quantum states via multi-qubit encoding and transmitted through a 2 × 2 MIMO system with varied diversity schemes. The spatially mapped qubits undergo the quantum Fourier transform (QFT) to form quantum OFDM subcarriers, with a cyclic prefix added before transmission over fading quantum channels. At the receiver, the cyclic prefix is removed, the inverse QFT is applied, and the quantum MIMO decoder reconstructs spatially diverged quantum states. Then, quantum decoding reconstructs the bitstreams, followed by channel decoding and source decoding to recover the final image. Experimental results show that the proposed quantum MIMO-OFDM system outperforms its classical counterpart across all evaluated diversity configurations. It achieves peak signal-to-noise ratio (PSNR) values up to 58.48 dB, structural similarity index measure (SSIM) up to 0.9993, and universal quality index (UQI) up to 0.9999 for JPEG; PSNR up to 70.04 dB, SSIM up to 0.9998, and UQI up to 0.9999 for HEIF; and near-perfect reconstruction with infinite PSNR, SSIM of 1, and UQI of 1 for uncompressed images under high channel noise. These findings establish quantum MIMO-OFDM as a promising architecture for high-fidelity, bandwidth-efficient quantum multimedia communication. Full article

Sparsity-driven regularization has undergone significant development in single-image restoration, particularly with the transition from handcrafted priors to trainable deep architectures. In this work, a geometric prior-enhanced deep image prior (DIP) framework, termed DIP-MC, is proposed that integrates mean curvature (MC) regularization to promote natural smoothness and structural coherence in reconstructed images. To strengthen the representational capacity of DIP, a self-attention module is incorporated between the encoder and decoder, enabling the network to capture long-range dependencies and preserve fine-scale textures. In contrast to total variation (TV), which frequently produces piecewise-constant artifacts and staircasing, MC regularization leverages curvature information, resulting in smoother transitions while maintaining sharp structural boundaries. DIP-MC is evaluated on standard grayscale and color image denoising and deblurring tasks using benchmark datasets including BSD68, Classic5, LIVE1, Set5, Set12, Set14, and the Levin dataset. Quantitative performance is assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics. Experimental results demonstrate that DIP-MC consistently outperformed the DIP-TV baseline with 26.49 PSNR and 0.9 SSIM. It achieved competitive performance relative to BM3D and EPLL models with 28.6 PSNR and 0.87 SSIM while producing visually more natural reconstructions with improved detail fidelity. Furthermore, the learning dynamics of DIP-MC are analyzed by examining update-cost behavior during optimization, visualizing the best-performing network weights, and monitoring PSNR and SSIM progression across training epochs. These evaluations indicate that DIP-MC exhibits superior stability and convergence characteristics. Overall, DIP-MC establishes itself as a robust, scalable, and geometrically informed framework for high-quality single-image restoration. Full article

Generative adversarial networks (GANs) typically require large datasets for effective training, which poses challenges for volumetric medical imaging tasks where data are scarce. This study addresses this limitation by extending adaptive discriminator augmentation (ADA) for three-dimensional (3D) StyleGAN2 to improve generative performance on limited volumetric data. The proposed 3D StyleGAN2-ADA redefines all 2D operations for volumetric processing and incorporates the full set of original augmentation techniques. Experiments are conducted on the NoduleMNIST3D dataset of lung CT scans containing 590 voxel-based samples across two classes. Two augmentation pipelines are evaluated—one using color-based transformations and another employing a comprehensive set of 3D augmentations including geometric, filtering, and corruption augmentations. Performance is compared against the same network and dataset without any augmentations at all by assessing generation quality with Kernel Inception Distance (KID) and 3D Structural Similarity Index Measure (SSIM). Results show that volumetric ADA substantially improves training stability and reduces the risk of a mode collapse, even under severe data constraints. A strong augmentation strategy improves the realism of generated 3D samples and better preserves anatomical structures relative to those without data augmentation. These findings demonstrate that adaptive 3D augmentations effectively enable high-quality synthetic medical image generation from extremely limited volumetric datasets. The source code and the weights of the networks are available in the GitHub repository. Full article

Reflectance normalization is critical for minimizing temporal discrepancies and facilitating reliable multi-temporal satellite analysis. However, this process is challenged by the risks of under-normalization and over-normalization, which stem from the inherent complexities of varying atmospheric conditions, data acquisition, and environmental dynamics. Under-normalization occurs when multi-temporal variations are insufficiently corrected, resulting in temporal reflectance inconsistencies. Over-normalization arises when overly aggressive adjustments suppress meaningful variability, such as seasonal and phenological patterns, thereby compromising data integrity. Effectively addressing these challenges is essential for preserving the spatial and temporal fidelity of satellite imagery, which is crucial for applications such as environmental monitoring and long-term change analysis. This study introduces a novel graph-based relaxation for reflectance normalization aimed at addressing issues of under- and over-normalization through a two-stage structural normalization strategy: intra-normalization and inter-normalization. A graph structure represents adjacency and similarity among image instances, enabling an iterative relaxation process to adjust reflectance values. In the proposed framework, the intra-normalization stage aligns images within the same reflectance group to preserve temporally local reflectance patterns, while the inter-normalization stage harmonizes reflectance across different groups, ensuring smooth temporal transitions and maintaining essential temporal variability. Experimental results with the metrics root mean squared error (RMSE) and Structural Similarity Index Measure (SSIM) demonstrate the effectiveness of the proposed method. Specifically, the proposed method achieves around 37% improvement measured by RMSE in the transition of two adjacent image groups compared with related normalization methods. Graph-based relaxation preserves seasonal dynamics, ensures smooth transitions, and improves vegetation indices, making it suitable for both short-term and long-term environmental change analysis. Full article

The escalating accumulation of waste printed circuit boards (WPCBs) underscores the urgent need for efficient recovery of valuable resources. Notably, WPCBs harbor a considerable number of intact electronic components that remain functional or could be repurposed. Nevertheless, the automated recognition and sorting of these components remain highly challenging, owing to their miniature dimensions, diverse model types, and the absence of publicly available, high-quality datasets. To address these challenges, this paper introduces a novel image dataset of discarded electronic components and proposes a deep learning-based data augmentation model that combines classical augmentation methods with DCGAN and SRGAN to achieve dataset size augmentation. This paper further conducts Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) evaluation on the generated images to ensure their suitability for downstream classification tasks. Experimental results demonstrate significant improvements in classification accuracy, with AlexNet, VGG19, ResNet18, ResNet101, and ResNet152 achieving increases of 6.6%, 9.7%, 4%, 5.4%, and 6.2%, respectively, compared to classical augmentation. This method enables precise identification to facilitate the downstream recovery of intact electronic components, thereby contributing to the conservation of natural resources and the effective mitigation of environmental pollution. Full article

Sudden Stratospheric Warmings (SSWs) are extreme polar atmospheric disturbances that significantly impact mid-latitude cold surges, but their early prediction remains a challenge for conventional numerical models. In this study, we propose a video prediction framework for SSW forecasting and introduce a Decoupled Spatiotemporal Memory Flow Network (DSMF-Net) to more effectively capture the dynamic evolution of stratospheric polar vortices. DSMF-Net separates spatial and temporal dependencies using specialized memory flow modules, enabling fine-grained modeling of vortex morphology and dynamic transitions. Experiments on representative SSW events from 2018 to 2021 show that DSMF-Net can reliably predict SSW occurrences up to 20 days in advance while accurately replicating the evolution of polar vortex structures. Compared to baseline models such as the Predictive Recurrent Neural Network (PredRNN) and Motion Recurrent Neural Network (MotionRNN), our method achieves consistent improvements across various metrics, with average gains of 10.5% in Mean Squared Error (MSE) and 6.4% in Mean Absolute Error (MAE) and a 0.7% increase in the Structural Similarity Index Measure (SSIM). These findings underscore the potential of deep video prediction frameworks to improve medium-range stratospheric forecasts and bridge the gap between data-driven models and atmospheric dynamics. Full article

With the rapid proliferation of digital multimedia in resource-constrained Internet of Things (IoT) environments, there is growing demand for efficient image compression combined with secure data embedding. Existing Vector Quantization (VQ)-based Reversible Data Hiding (RDH) methods prioritize embedding capacity while neglecting reconstruction fidelity, often introducing noticeable quality degradation in edge regions—unacceptable for high-fidelity applications such as medical imaging and forensic analysis. This paper proposes a lightweight RDH framework with a once-offline trained VQ codebook that simultaneously performs secure data embedding and visual quality enhancement for VQ-compressed images. Quality Improvement Codes (QIC) are generated from pixel-wise residuals between original and VQ-decompressed images and embedded into the VQ index table using a novel Recoding Index Value (RIV) mechanism without additional transmission overhead. Sobel edge detection identifies perceptually sensitive blocks for targeted enhancement. Comprehensive experiments on ten standard test images across multiple resolutions (256 × 256, 512 × 512) and codebook sizes (64–1024) demonstrate Peak Signal-to-Noise Ratio (PSNR) gains of +4 to +5.39 dB and Structural Similarity Index Measure (SSIM) improvements of +4.12% to +9.86%, with embedding capacities approaching 100 Kbits. The proposed approach consistently outperforms existing methods in both image quality and payload capacity while eliminating computational overhead of deep learning models, making it highly suitable for resource-constrained edge devices and real-time multimedia security applications. Full article

Underwater object detection is critical for marine resource utilization, ecological monitoring, and maritime security, yet it remains constrained by optical degradation, high energy consumption, and vulnerability to adversarial perturbations. To address these challenges, this study proposes an Adaptive Vision Transformer (A-ViT)-based detection framework. At the hardware level, a systematic power-modeling and endurance-estimation scheme ensures feasibility across shallow- and deep-water missions. Through the super-resolution reconstruction based on the Hybrid Attention Transformer (HAT) and the staged enhancement with the Deep Initialization and Deep Inception and Channel-wise Attention Module (DICAM), the image quality was significantly improved. Specifically, the Peak Signal-to-Noise Ratio (PSNR) increased by 74.8%, and the Structural Similarity Index (SSIM) improved by 375.8%. Furthermore, the Underwater Image Quality Measure (UIQM) rose from 3.00 to 3.85, while the Underwater Color Image Quality Evaluation (UCIQE) increased from 0.550 to 0.673, demonstrating substantial enhancement in both visual fidelity and color consistency. Detection accuracy is further enhanced by an improved YOLOv11-Coordinate Attention–High-order Spatial Feature Pyramid Network (YOLOv11-CA_HSFPN), which attains a mean Average Precision at Intersection over Union 0.5 (mAP@0.5) of 56.2%, exceeding the baseline YOLOv11 by 1.5 percentage points while maintaining 10.5 ms latency. The proposed A-ViT + ROI reduces inference latency by 27.3% and memory usage by 74.6% when integrated with YOLOv11-CA_HSFPN and achieves up to 48.9% latency reduction and 80.0% VRAM savings in other detectors. An additional Image-stage Attack QuickCheck (IAQ) defense module reduces adversarial-attack-induced latency growth by 33–40%, effectively preventing computational overload. Full article

Automobile license plate image generation represents a pivotal technology for the development of intelligent transportation systems. However, existing methods are constrained by their inability to simultaneously preserve geometric structure and radiometric properties of both license plates and characters. To overcome this limitation, we propose a novel framework for generating geometrically and radiometrically consistent license plate images. The proposed radiometric enhancement framework integrates two specialized modules, which are precise geometric rectification and radiometric property learning. The precise geometric rectification module exploits the perspective transformation consistency between character regions and license plate boundaries. By employing a feature matching algorithm based on character endpoint correspondence, this module achieves precise plate rectification, thereby establishing a geometric foundation for maintaining character structural integrity in generated images. The radiometric property learning module implements a precise character inpainting strategy with fluctuation compensation inpainting to reconstruct background regions, followed by a character-wise style transfer approach to ensure both geometric and radiometric consistency with realistic automobile license plates. Furthermore, we introduce a physical validation and evaluation method to quantitatively assess image quality. Comprehensive evaluation on real-world datasets demonstrate that our method achieves superior performance, with a peak signal-to-noise ratio (PSNR) of 13.83 dB and a structural similarity index measure (SSIM) of 0.57, representing significant improvements over comparative methods in preserving both structural integrity and radiometric properties. This framework effectively enhances the visual fidelity and reliability of generated automobile license plate images, thereby providing high-quality data for intelligent transportation recognition systems while advancing license plate image generation technology. Full article

Remote sensing image super-resolution (SR) plays a crucial role in enhancing the quality and resolution of satellite and aerial imagery, which is essential for various applications, including environmental monitoring and urban planning. While recent image super-resolution networks have achieved strong results, remote-sensing images present domain-specific challenges—complex spatial distribution, large cross-scale variations, and dynamic topographic effects—that can destabilize multi-scale fusion and limit the direct applicability of generic SR models. These features make it difficult for single-scale feature extraction methods to fully capture the complex structure, leading to the presence of artifacts and structural distortion in the reconstructed remote sensing images. Therefore, new methods are needed to overcome these challenges and improve the accuracy and detail fidelity of remote sensing image super-resolution reconstruction. This paper proposes a novel Multi-scale Windowed Spatial and Channel Attention Network (MSWSCAN) for high-fidelity remote sensing image super-resolution. The proposed method combines multi-scale feature extraction, window-based spatial attention, and channel attention mechanisms to effectively capture both global and local image features while addressing the challenges of fine details and structural distortion. The network is evaluated on several benchmark datasets, including WHU-RS19, UCMerced and RSSCN7, where it demonstrates superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to state-of-the-art methods. The results show that the MSWSCAN not only enhances texture details and edge sharpness but also reduces reconstruction artifacts. To address cross-scale variations and dynamic topographic effects that cause texture drift in multi-scale SR, we combine windowed spatial attention to preserve local geometry with a channel-aware fusion layer (FFL) that reweights multi-scale channels. This stabilizes cross-scale aggregation at a runtime comparable to DAT and yields sharper details on heterogeneous land covers. Averaged over WHU–RS19, RSSCN7, and UCMerced_LandUse at ×2/×3/×4, MSWSCAN improves PSNR (peak signal-to-noise ratio, dB)/SSIM (structural similarity index measure, 0–1) by +0.10 dB/+0.0038 over SwinIR and by +0.05 dB/+0.0017 over DAT. In conclusion, the proposed MSWSCAN achieves state-of-the-art performance in remote sensing image SR, offering a promising solution for high-quality image enhancement in remote sensing applications. Full article

Energy window selection is a critical parameter for optimizing planar gamma image quality in nuclear medicine. In this study, we developed dedicated nuclear medicine phantoms using 3D printing technology to evaluate the impact of varying energy window levels on image quality. Three types of phantoms—a Derenzo phantom with six different sphere diameters, a modified Hoffman phantom incorporating lead for attenuation, and a quadrant bar phantom with four bar thicknesses constructed from bronze filament—were fabricated using Fusion 360 and an Ultimaker S5 3D printer with PLA and bronze-based materials. Planar images were acquired using 37 MBq of Tc-99m for 60 s at energy windows centered at 122, 140, and 159 keV. Quantitative assessments included contrast-to-noise ratio (CNR), coefficient of variation (COV), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), comparing all images with the 140 keV image as the reference. The results showed a consistent decline in image quality at 122 keV and 159 keV, with the highest CNR, lowest COV, and optimal PSNR/SSIM values obtained at 140 keV. In visual analysis using the quadrant bar phantom, thinner bars were more clearly discernible at 140 keV than at other energy levels. These findings demonstrate that the application of an appropriate energy window—particularly 140 keV for Tc-99m—substantially improves image quality in planar gamma imaging. The use of customized, material-specific 3D-printed phantoms also enables flexible, reproducible evaluation protocols for energy-dependent imaging optimization and quality assurance in clinical nuclear medicine. Full article