Search Results (1,137)

Conventional grain monitoring systems often rely on isolated data points (e.g., point-based temperature measurements), limiting holistic condition assessment. This study proposes a novel Mechanism and Data Driven (MDD) framework that integrates physical mechanisms with real-time sensor data. The framework quantitatively analyzes solar radiation and external air temperature effects on silo boundaries and introduces a novel interpolation-optimized model parameter initialization technique to enable comprehensive grain condition perception. Rigorous multidimensional validation confirms the method’s accuracy: The novel initialization technique achieved high precision, demonstrating only 1.89% error in Day-2 low-temperature zone predictions (27.02 m² measured vs. 26.52 m² simulated). Temperature fields were accurately reconstructed (≤0.5 °C deviation in YOZ planes), capturing spatiotemporal dynamics with ≤0.45 m² maximum low-temperature zone deviation. Cloud map comparisons showed superior simulation fidelity (SSIM > 0.97). Further analysis revealed a 22.97% reduction in total low-temperature zone area (XOZ plane), with Zone 1 (near south exterior wall) declining 27.64%, Zone 2 (center) 25.30%, and Zone 3 20.35%. For dynamic evolution patterns, high-temperature zones exhibit low moisture (<14%), while low-temperature zones retain elevated moisture (>14%). A strong positive correlation between temperature and relative humidity fields; temperature homogenization drives humidity uniformity. The framework enables holistic monitoring, providing actionable insights for smart ventilation control, condensation risk warnings, and mold prevention. It establishes a robust foundation for intelligent grain storage management, ultimately reducing post-harvest losses. Full article

Currently, the customs inspection of wood-boring pests in timber still primarily relies on manual visual inspection, which involves observing insect holes on the timber surface and splitting the timber for confirmation. However, this method has significant drawbacks such as long detection time, high labor cost, and accuracy relying on human experience, making it difficult to meet the practical needs of efficient and intelligent customs quarantine. To address this issue, this paper develops a rapid identification system based on the peristaltic signals of wood-boring pests through the PyQt framework. The system employs a deep learning model with multi-attention mechanisms, namely the Residual Denoising Vision Network (RDVNet). Firstly, a LabVIEW-based hardware–software system is used to collect pest peristaltic signals in an environment free of vibration interference. Subsequently, the original signals are clipped, converted to audio format, and mixed with external noise. Then signal features are extracted through three cepstral feature extraction methods Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) and input into the model. In the experimental stage, this paper compares the denoising module of RDVNet (de-RDVNet) with four classic denoising models under five noise intensity conditions. Finally, it evaluates the performance of RDVNet and four other noise reduction classification models in classification tasks. The results show that PNCC has the most comprehensive feature extraction capability. When PNCC is used as the model input, de-RDVNet achieves an average peak signal-to-noise ratio (PSNR) of 29.8 and a Structural Similarity Index Measure (SSIM) of 0.820 in denoising experiments, both being the best among the comparative models. In classification experiments, RDVNet has an average F1 score of 0.878 and an accuracy of 92.8%, demonstrating the most excellent performance. Overall, the application of this system in customs timber quarantine can effectively improve detection efficiency and reduce labor costs and has significant practical value and promotion prospects. Full article

Digital twins are essential for the real-time health management and monitoring of aeroengines, and the in situ three-dimensional (3D) reconstruction technology of key components of aeroengines is an important support for the construction of a digital twin model. In this paper, an in situ high-fidelity 3D reconstruction method, named PoseNeRF, for aeroengine blades based on the joint optimization of pose and neural radiance field (NeRF), is proposed. An aeroengine blades background filtering network based on complex network theory (ComBFNet) is designed to filter out the useless background information contained in the two-dimensional (2D) images and improve the fidelity of the 3D reconstruction of blades, and the mean intersection over union (mIoU) of the network reaches 95.5%. The joint optimization loss function, including photometric loss, depth loss, and point cloud loss is proposed. The method solves the problems of excessive blurring and aliasing artifacts, caused by factors such as smooth blade surface and weak texture information in 3D reconstruction, as well as the cumulative error problem caused by camera pose pre-estimation. The PSNR, SSIM, and LPIPS of the 3D reconstruction model proposed in this paper reach 25.59, 0.719, and 0.239, respectively, which are superior to other general models. Full article

Background/Objectives: Preclinical chest computed tomography (CT) imaging in small animals is often limited by low resolution due to scan time and dose constraints, which hinders accurate detection of subtle lesions. Traditional super-resolution (SR) metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), may not adequately reflect clinical interpretability. We aimed to evaluate whether deep learning-based SR models could enhance image quality and lesion detectability in rat chest CT, balancing quantitative metrics with radiologist assessment. Methods: We retrospectively analyzed 222 chest CT scans acquired from polyhexamethylene guanidine phosphate (PHMG-p) exposure studies in Sprague Dawley rats. Three SR models were implemented and compared: single-image SR (SinSR), segmentation-guided SinSR with lung cropping (SinSR3), and omni-super-resolution (OmniSR). Models were trained on rat CT data and evaluated using PSNR and SSIM. Two board-certified thoracic radiologists independently performed blinded evaluations of lesion margin clarity, nodule detectability, image noise, artifacts, and overall image quality. Results: SinSR1 achieved the highest PSNR (33.64 ± 1.30 dB), while SinSR3 showed the highest SSIM (0.72 ± 0.08). Despite lower PSNR (29.21 ± 1.46 dB), OmniSR received the highest radiologist ratings for lesion margin clarity, nodule detectability, and overall image quality (mean score 4.32 ± 0.41, κ = 0.74). Reader assessments diverged from PSNR and SSIM, highlighting the limited correlation between conventional metrics and clinical interpretability. Conclusions: Deep learning-based SR improved visualization of rat chest CT images, with OmniSR providing the most clinically interpretable results despite modest numerical scores. These findings underscore the need for reader-centered evaluation when applying SR techniques to preclinical imaging. Full article

In this paper, we propose a novel image restoration framework that integrates optimal control techniques with the Hamilton–Jacobi–Bellman (HJB) equation. Motivated by models from production planning, our method restores degraded images by balancing an intervention cost against a state-dependent penalty that quantifies the loss of critical image information. Under the assumption of radial symmetry, the HJB equation is reduced to an ordinary differential equation and solved via a shooting method, from which the optimal feedback control is derived. Numerical experiments, supported by extensive parameter tuning and quality metrics such as PSNR and SSIM, demonstrate that the proposed framework achieves significant improvement in image quality. The results not only validate the theoretical model but also suggest promising directions for future research in adaptive and hybrid image restoration techniques. Full article

Background/Objectives: Video laryngoscopy is one of the primary methods used by otolaryngologists for detecting and classifying laryngeal lesions. However, the diagnostic process of these images largely relies on clinicians’ visual inspection, which can lead to overlooked small structural changes, delayed diagnosis, and interpretation errors. Methods: AI-based approaches are becoming increasingly critical for accelerating early-stage diagnosis and improving reliability. This study proposes a hybrid Convolutional Neural Network (CNN) architecture that eliminates repetitive and clinically insignificant frames from videos, utilizing only meaningful key frames. Video data from healthy individuals, patients with vocal fold nodules, and those with vocal fold polyps were summarized using three different threshold values with the Structural Similarity Index Measure (SSIM). Results: The resulting key frames were then classified using a hybrid CNN. Experimental findings demonstrate that selecting an appropriate threshold can significantly reduce the model’s memory usage and processing load while maintaining accuracy. In particular, a threshold value of 0.90 provided richer information content thanks to the selection of a wider variety of frames, resulting in the highest success rate. Fine-tuning the last 20 layers of the MobileNetV2 and Xception backbones, combined with the fusion of extracted features, yielded an overall classification accuracy of 98%. Conclusions: The proposed approach provides a mechanism that eliminates unnecessary data and prioritizes only critical information in video-based diagnostic processes, thus helping physicians accelerate diagnostic decisions and reduce memory requirements. Full article

Conventional fundus (CF) and ultrawidefield fundus (UF) imaging are two primary modalities widely used in ophthalmology. Despite the complementary use of both imaging modalities in clinical practice, existing research on fundus image translation has yet to reach clinical viability and often lacks the necessary accuracy and detail required for practical medical use. Additionally, collecting paired UFI-CFI data from the same patients presents significant limitations, and unpaired learning-based generative models frequently suffer from distortion phenomena, such as hallucinations. This study introduces an enhanced modality transformation method to improve the diagnostic support capabilities of deep learning models in ophthalmology. The proposed method translates UF images (UFIs) into CF images (CFIs), potentially replacing the dual-imaging approach commonly used in clinical practice. This replacement can significantly reduce financial and temporal burdens on patients. To achieve this, this study leveraged UFI–CFI image pairs obtained from the same patient on the same day. This approach minimizes information distortion and accurately converts the two modalities. Our model employs scaled feature registration and distorted vessel correction methods to align UFI–CFI pairs effectively. The generated CFIs not only enhance image quality and better represent the retinal area compared to existing methods but also effectively preserve disease-related details from UFIs, aiding in accurate diagnosis. Furthermore, compared with existing methods, our model demonstrated a substantial 18.2% reduction in MSE, a 7.2% increase in PSNR, and a 12.7% improvement in SSIM metrics. Notably, our results show that the generated CFIs are nearly indistinguishable from the real CFIs, as confirmed by ophthalmologists. Full article

We aimed to investigate the utility of peritumoral edema-derived radiomic features from magnetic resonance imaging (MRI) image weights and fused MRI sequences for enhancing the performance of machine learning-based glioma grading. The present study utilized the Multimodal Brain Tumor Segmentation Challenge 2023 (BraTS 2023) dataset. Laplacian Re-decomposition (LRD) was employed to fuse multimodal MRI sequences. The fused image quality was evaluated using the Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. A comprehensive set of radiomic features was subsequently extracted from peritumoral edema regions using PyRadiomics. The Boruta algorithm was applied for feature selection, and an optimized classification pipeline was developed using the Tree-based Pipeline Optimization Tool (TPOT). Model performance for glioma grade classification was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC) parameters. Analysis of fused image quality metrics confirmed that the LRD method produces high-quality fused images. From 851 radiomic features extracted from peritumoral edema regions, the Boruta algorithm selected different sets of informative features in both standard MRI and fused images. Subsequent TPOT automated machine learning optimization analysis identified a fine-tuned Stochastic Gradient Descent (SGD) classifier, trained on features from T₁Gd+FLAIR fused images, as the top-performing model. This model achieved superior performance in glioma grade classification (Accuracy = 0.96, Precision = 1.0, Recall = 0.94, F1-Score = 0.96, AUC = 1.0). Radiomic features derived from peritumoral edema in fused MRI images using the LRD method demonstrated distinct, grade-specific patterns and can be utilized as a non-invasive, accurate, and rapid glioma grade classification method. Full article

High dynamic range (HDR) imaging aims to overcome the limited dynamic range of traditional imaging systems and achieve effective restoration of the brightness and color of the real world. In recent years, single-image HDR (SI-HDR) reconstruction technology has become a research hotspot due to its simple acquisition process and applicability to dynamic scenes. This paper proposes an improved SI-HDR reconstruction method based on HDRUNet, which systematically integrates channel, spatial attention mechanism, brightness expansion, and color-enhancement branches, and constructs an adaptive multi-component loss function. This effectively enhances the detail restoration in extreme exposure areas and improves the overall color expressiveness. Experiments on public datasets such as NTIRE 2021, VDS, and HDR-Eye show that the proposed method outperforms the mainstream SI-HDR methods in terms of PSNR, SSIM, and VDP evaluation metrics. It performs particularly well in complex scenarios, demonstrating greater robustness and generalization ability. Full article

This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a bespoke model architecture that integrates attention mechanisms with visual reasoning through a generalized conditioning layer. A novel mechanism that enables the steering of urban pattern generation through the use of statistical input distributions, the development of a novel and comprehensive training dataset, meticulously derived from open-source geospatial data of Berlin. Our model is trained using a hybrid loss function, combining adversarial, focal and L1 losses to ensure perceptual realism, address challenging fine-grained features, and enforce pixel-level accuracy. Model performance was assessed through a combination of qualitative visual analysis and quantitative evaluation using metrics such as Kullback–Leibler Divergence (KL Divergence), Structural Similarity Index (SSIM), and Dice Coefficient. The proposed approach has demonstrated effectiveness in generating realistic and spatially coherent urban patterns, with promising potential for controllability. In addition to showcasing its strengths, we also highlight the limitations and outline future directions for advancing future work. Full article

As a vital carrier of China’s intangible cultural heritage, restoring damaged embroidery fabrics is essential for the sustainable preservation of cultural relics. However, existing methods face persistent challenges, such as mask pattern mismatches and restoration size constraints. To address these gaps, this study proposes an embroidery image restoration framework based on enhanced generative adversarial networks (GANs). Specifically, the framework integrates a U-Net generator with a multi-scale discriminator augmented by an attention mechanism and dual-path residual blocks to significantly enhance texture generation. Furthermore, fabric damage was classified into three categories (hole-shaped, crease-shaped, and block-shaped), with complex patterns simulated through dynamic randomization. Grid-based overlapping segmentation and pixel fusion techniques enable arbitrary-dimensional restoration. Quantitative evaluations demonstrated exceptional performance in complex texture restoration, achieving a structural similarity index (SSIM) of 0.969 and a peak signal-to-noise ratio (PSNR) of 32.182 dB. Complementarily, eye-tracking experiments revealed no persistent visual fixation clusters in the restored regions, confirming perceptual reliability. This approach establishes an efficient digital conservation pathway that promotes resource-efficient and sustainable heritage conservation. Full article

Material decomposition in X-ray imaging is essential for enhancing tissue differentiation and reducing the radiation dose, but the clinical adoption of photon-counting detectors (PCDs) is limited by their high cost and technical complexity. To address this, we propose Dual-head Pix2Pix, a PCD-guided deep learning framework that enables simultaneous iodine and bone decomposition from single-energy X-ray projections acquired with conventional energy-integrating detectors. The model was trained and tested on 1440 groups of energy-integrating detector (EID) projections with their corresponding iodine/bone decomposition images. Experimental results demonstrate that the Dual-head Pix2Pix outperforms baseline models. For iodine decomposition, it achieved a mean absolute error (MAE) of 5.30 ± 1.81, representing an ~10% improvement over Pix2Pix (5.92) and a substantial advantage over CycleGAN (10.39). For bone decomposition, the MAE was reduced to 9.55 ± 2.49, an ~6% improvement over Pix2Pix (10.18). Moreover, Dual-head Pix2Pix consistently achieved the highest MS-SSIM, PSNR, and Pearson correlation coefficients across all benchmarks. In addition, we performed a cross-domain validation using projection images acquired from a conventional EID-CT system. The results show that the model successfully achieved the effective separation of iodine and bone in this new domain, demonstrating a strong generalization capability beyond the training distribution. In summary, Dual-head Pix2Pix provides a cost-effective, scalable, and hardware-friendly solution for accurate dual-material decomposition, paving the way for the broader clinical and industrial adoption of material-specific imaging without requiring PCDs. Full article

The evaluation and enhancement of image aesthetics play a pivotal role in the development of visual media, impacting fields including photography, design, and computer vision. Composition, a key factor shaping visual aesthetics, significantly influences an image’s vividness and expressiveness. However, existing image optimization methods face practical challenges: compression-induced distortion, imprecise object extraction, and cropping-caused unnatural proportions or content loss. To tackle these issues, this paper proposes an image aesthetic evaluation with composition and similarity (IACS) method that harmonizes composition aesthetics and image similarity through a unified function. When evaluating composition aesthetics, the method calculates the distance between the main semantic line (or salient object) and the nearest rule-of-thirds line or central line. For images featuring prominent semantic lines, a modified Hough transform is utilized to detect the main semantic line, while for images containing salient objects, a salient object detection method based on luminance channel salience features (LCSF) is applied to determine the salient object region. In evaluating similarity, edge similarity measured by the Canny operator is combined with the structural similarity index (SSIM). Furthermore, we introduce a Framework for Image Aesthetic Evaluation with Composition and Similarity-Based Optimization (FIACSO), which uses semantic segmentation and generative adversarial networks (GANs) to optimize composition while preserving the original content. Compared with prior approaches, the proposed method improves both the aesthetic appeal and fidelity of optimized images. Subjective evaluation involving 30 participants further confirms that FIACSO outperforms existing methods in overall aesthetics, compositional harmony, and content integrity. Beyond methodological contributions, this study also offers practical value: it supports photographers in refining image composition without losing context, assists designers in creating balanced layouts with minimal distortion, and provides computational tools to enhance the efficiency and quality of visual media production. Full article

We present an automated and fully reproducible pipeline for restoring motion-smeared Mars Express SRC images of Phobos. A one-dimensional motion point spread function (PSF) is derived directly from SPICE geometry and microsecond-precision exposure timing, and Wiener deconvolution (SNR = 16 dB) is applied to recover image sharpness. Tested on 14 images from 4 orbits spanning slant distances of 52–292 km, exposures of 14–20 milliseconds, sampling of 0.47–2.7 m/pixel, and PSF lengths of 11–119 pixels, the method achieves up to 31.7 dB PSNR, 0.78 SSIM, and positive sharpness gains across all cases. The restored images reveal sub-meter surface features previously obscured by motion blur, with residual energy reduced relative to the acquisition model. The workflow relies solely on open data and open-source tools (ISIS, ALE/SpiceyPy, OpenCV), requires no star-field calibration, and generalizes to other motion-degraded planetary datasets, providing a fully transparent and reproducible solution for high-resolution planetary imaging. Full article

High-resolution (HR) medical images provide clearer anatomical details and facilitate early disease diagnosis, yet acquiring HR scans is often limited by imaging conditions, device capabilities, and patient factors. We propose a transform domain deep multiscale feature fusion generative adversarial network (MSFF-GAN) for medical image super-resolution (SR). Considering the advantages of generative adversarial networks (GANs) and convolutional neural networks (CNNs), MSFF-GAN integrates a deep multi-scale convolution network into the GAN generator, which is composed primarily of a series of cascaded multi-scale feature extraction blocks in a coarse-to-fine manner to restore the medical images. Two tailored blocks are designed: a multiscale information distillation (MSID) block that adaptively captures long- and short-path features across scales, and a granular multiscale (GMS) block that expands receptive fields at fine granularity to strengthen multiscale feature extraction with reduced computational cost. Unlike conventional methods that predict HR images directly in the spatial domain, which often yield excessively smoothed outputs with missing textures, we formulate SR as the prediction of coefficients in the non-subsampled shearlet transform (NSST) domain. This transform domain modeling enables better preservation of global anatomical structure and local texture details. The predicted coefficients are inverted to reconstruct HR images, and the transform domain subbands are also fed to the discriminator to enhance its discrimination ability and improve perceptual fidelity. Extensive experiments on medical image datasets demonstrate that MSFF-GAN outperforms state-of-the-art approaches in structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), while more effectively preserving global anatomy and fine textures. These results validate the effectiveness of combining multiscale feature fusion with transform domain prediction for high-quality medical image super-resolution. Full article