Search Results (135)

To overcome the limitations of low texture accuracy in traditional sculpture color restoration methods, this study proposes an improved Deep Convolutional Generative Adversarial Network (DCGAN) model incorporating a dual attention mechanism (spatial and channel attention) and a channel converter to enhance restoration quality. First, the theoretical foundations of the DCGAN algorithm and its key components (generator, discriminator, etc.) are systematically introduced. Subsequently, a DCGAN-based application model for sculpture color restoration is developed. The generator employs a U-Net architecture integrated with a dual attention module and a channel converter, enhancing both local feature representation and global information capture. Meanwhile, the discriminator utilizes an image region segmentation approach to optimize the assessment of consistency between restored and original regions. The loss function follows a joint optimization strategy, combining perceptual loss, adversarial loss, and structural similarity index (SSIM) loss, ensuring superior restoration performance. In the experiments, mean square error (MSE), peak signal-to-noise ratio (PSNR), and SSIM were used as evaluation metrics, and sculpture color restoration tests were conducted on an Intel Xeon workstation. The performance of the proposed model was compared against the traditional DCGAN and other restoration models. The experimental results demonstrate that the improved DCGAN outperforms traditional methods across all evaluation metrics, and compared to traditional DCGAN, the proposed model achieves significantly higher SSIM and PSNR, while reducing MSE. Compared to other restoration models, PSNR and SSIM are further enhanced, MSE is reduced, and the visual consistency between the restored and undamaged areas is significantly improved, with richer texture details. Full article

Motor Imagery (MI) based Brain Computer Interfaces (BCIs) have promising applications in neurorehabilitation for individuals who have lost mobility and control over parts of their body due to brain injuries, such as stroke patients. Accurately classifying MI tasks is essential for effective BCI performance, but this task remains challenging due to the complex and non-stationary nature of EEG signals. This study aims to improve the classification of left and right-hand MI tasks by utilizing high-resolution time-frequency features extracted from EEG signals, enhanced with deep learning-based data augmentation techniques. We propose a novel deep learning framework named the Generalized Wavelet Transform-based Deep Convolutional Network (GDC-Net), which integrates multiple components. First, EEG signals recorded from the C3, C4, and Cz channels are transformed into detailed time-frequency representations using the Generalized Morse Wavelet Transform (GMWT). The selected features are then expanded using a Deep Convolutional Generative Adversarial Network (DCGAN) to generate additional synthetic data and address data scarcity. Finally, the augmented feature maps data are subsequently fed into a hybrid CNN-LSTM architecture, enabling both spatial and temporal feature learning for improved classification. The proposed approach is evaluated on the BCI Competition IV dataset 2b. Experimental results showed that the mean classification accuracy and Kappa value are 89.24% and 0.784, respectively, making them the highest compared to the state-of-the-art algorithms. The integration of GMWT and DCGAN significantly enhances feature quality and model generalization, thereby improving classification performance. These findings demonstrate that GDC-Net delivers superior MI classification performance by effectively capturing high-resolution time-frequency dynamics and enhancing data diversity. This approach holds strong potential for advancing MI-based BCI applications, especially in assistive and rehabilitation technologies. Full article

Stationary inverse-geometry digital tomosynthesis (s-IGDT) causes truncation artifacts in reconstructed images due to its geometric characteristics. This study introduces a deep convolutional generative adversarial network (DCGAN)-based out-painting method for mitigating truncation artifacts in s-IGDT images. The proposed network employed an encoder–decoder architecture for the generator, and a dilated convolution block was added between the encoder and decoder. A dual-discriminator was used to distinguish the artificiality of generated images for truncated and non-truncated regions separately. During network training, the generator was able to selectively learn a target task for the truncated regions using binary mask images. The performance of the proposed method was compared to conventional methods in terms of signal-to-noise ratio (SNR), normalized root-mean-square error (NRMSE), peak SNR (PSNR), and structural similarity (SSIM). The results showed that the proposed method led to a substantial reduction in truncation artifacts. On average, the proposed method achieved 62.31, 16.66, and 14.94% improvements in the SNR, PSNR, and SSIM, respectively, compared to the conventional methods. Meanwhile, the NRMSE values were reduced by an average of 37.22%. In conclusion, the proposed out-painting method can offer a promising solution for mitigating truncation artifacts in s-IGDT images and improving the clinical availability of the s-IGDT. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

The gearbox, a key transmission device in industrial applications, can cause severe vibrations or failures when anomalies occur. With increasing industrial automation complexity, precise anomaly detection is crucial. This paper introduces DCCopGAN, a novel unsupervised framework that uses a deep convolutional copula-generative adversarial network for unsupervised multi-sensor anomaly detection in industrial gearboxes. Firstly, a Deep Convolutional Generative Adversarial Network (DCGAN) generator is trained on high-dimensional normal operational data from multi-sensors to learn its underlying distribution, enabling the calculation of reconstruction errors for input samples. Then, these reconstruction errors are analyzed by Copula-Based Outlier Detection (CopOD), an efficient non-parametric technique, to identify anomalies. In the testing phase, reconstruction errors for test samples are similarly computed, normalized, and then evaluated by the CopOD mechanism to assign anomaly scores and detect deviations from normal behavior. The proposed DCCopGAN framework has been validated on a real gearbox dataset, where experimental results demonstrate its superior anomaly detection performance over other methods. Full article

Background:Generative Adversarial Networks (GANs), thanks to their great versatility, have a plethora of applications in biomedical imaging with the goal of simulating complex pathological conditions and creating clinical data used for training advanced machine learning models. The ability to generate high-quality synthetic clinical data not only addresses issues related to the scarcity of annotated bioimages but also supports the continuous improvement of diagnostic tools. Method: We propose a two-step method aimed to detect whether a bioimage can be considered fake or real. The first step is related to bioimage generation using a Deep Convolutional GAN, while the second step involves the training and testing of a set of machine learning models aimed to distinguish between real and generated bioimages. Results: We evaluate our approach by exploiting six different datasets. We observe notable results, demonstrating the ability of Deep Convolutional GAN to generate realistic synthetic images for some specific bioimages. However, for other bioimages, the accuracy does not align with the expected trend, indicating challenges in generating images that closely resemble real ones. Conclusions: This study highlights both the potential and limitations of GAN in generating realistic bioimages. Future work will focus on improving generation quality and detection accuracy across different datasets. Full article

The acquisition of real-world sonar data necessitates substantial investments of manpower, material resources, and financial capital, rendering it challenging to obtain sufficient authentic samples for sonar-related research tasks. Consequently, sonar image simulation technology has become increasingly vital in the field of sonar data analysis. Traditional sonar simulation methods predominantly focus on low-level physical modeling, which often suffers from limited image controllability and diminished fidelity in multi-category and multi-background scenarios. To address these limitations, this paper proposes a Conditional Dual-Branch Attention Generative Adversarial Network (CDBA-GAN). The framework comprises three key innovations: The conditional information fusion module, dual-branch attention feature fusion mechanism, and cross-layer feature reuse. By integrating encoded conditional information with the original input data of the generative adversarial network, the fusion module enables precise control over the generation of sonar images under specific conditions. A hierarchical attention mechanism is implemented, sequentially performing channel-level and pixel-level attention operations. This establishes distinct weight matrices at both granularities, thereby enhancing the correlation between corresponding elements. The dual-branch attention features are fused via a skip-connection architecture, facilitating efficient feature reuse across network layers. The experimental results demonstrate that the proposed CDBA-GAN generates condition-specific sonar images with a significantly lower Fréchet inception distance (FID) compared to existing methods. Notably, the framework exhibits robust imaging performance under noisy interference and outperforms state-of-the-art models (e.g., DCGAN, WGAN, SAGAN) in fidelity across four categorical conditions, as quantified by FID metrics. Full article

Predicting the motion of handwritten digits in video sequences is challenging due to complex spatiotemporal dependencies, variable writing styles, and the need to preserve fine-grained visual details—all of which are essential for real-time handwriting recognition and digital learning applications. In this context, our study aims to develop a robust predictive framework that can accurately forecast digit trajectories while preserving structural integrity. To address these challenges, we propose a novel video prediction architecture integrating ConvCARU with a modified DCGAN to effectively separate the background from the foreground. This ensures the enhanced extraction and preservation of spatial and temporal features through convolution-based gating and adaptive fusion mechanisms. Based on extensive experiments conducted on the MNIST dataset, which comprises 70 K pixel images, our approach achieves an SSIM of 0.901 and a PSNR of 29.31 dB. This reflects a statistically significant improvement in PSNR of +0.20 dB (p < 0.05) compared to current state-of-the-art models, thus demonstrating its superior capability in maintaining consistent structural fidelity in predicted video frames. Furthermore, our framework performs better in terms of computational efficiency, with lower memory consumption compared to most other approaches. This underscores its practicality for deployment in real-time, resource-constrained applications. These promising results consequently validate the effectiveness of our integrated ConvCARU–DCGAN approach in capturing fine-grained spatiotemporal dependencies, positioning it as a compelling solution for enhancing video-based handwriting recognition and sequence forecasting. This paves the way for its adoption in diverse applications requiring high-resolution, efficient motion prediction. Full article

The iterative process of urban development often produces fragmented renewal zones that disrupt the continuity of urban morphology, undermining both cultural identity and economic cohesion. Addressing this challenge, this study proposes a generative design framework based on Deep Convolutional Generative Adversarial Networks (DCGANs) to predict and regenerate urban morphology in alignment with existing spatial contexts. A dataset was constructed from highly urbanized city centers and used to train a DCGAN model. To evaluate the model performance, seven landscape pattern indices—LPI, PLAND, LSI, MPFD, AI, PLADJ, and NP—were employed to quantify changes in scale, shape, compactness, fragmentation, and spatial adjacency. Results show that the model accurately predicts morphological patterns and captures underlying spatial logic in developed urban areas, demonstrating strong sensitivity to local form characteristics, and enhancing the feasibility of sustainable urban renewal. Nonetheless, the model’s generalizability is constrained by inter-city morphological heterogeneity, highlighting the need for region-specific adaptation. This work contributes a data-driven approach to urban morphology research and offers a scalable framework for form-based, sustainability-oriented urban design. Full article

Mapping urban pluvial flooding (UPF) in data-scarce regions poses significant challenges, particularly when drainage systems are inadequate or outdated. These limitations hinder effective flood mitigation and risk assessment. This study proposes an innovative approach to address these challenges by integrating deep learning (DL) models with traditional methods. First, deep convolutional generative adversarial networks (DCGANs) were employed to enhance drainage network data generation. Second, deep recurrent neural networks (DRNNs) and multi-criteria decision analysis (MCDA) methods were implemented to assess UPF. The study compared the performance of these approaches, highlighting the potential of DL models in providing more accurate and robust flood mapping outcomes. The methodology was applied to Lahore, Pakistan—a rapidly urbanizing and data-scarce region frequently impacted by UPF during monsoons. High-resolution ALOS PALSAR DEM data were utilized to extract natural drainage networks, while synthetic datasets generated by GANs addressed the lack of historical flood data. Results demonstrated the superiority of DL-based approaches over traditional MCDA methods, showcasing their potential for broader applicability in similar regions worldwide. This research emphasizes the role of DL models in advancing urban flood mapping, providing valuable insights for urban planners and policymakers to mitigate flooding risks and improve resilience in vulnerable regions. Full article

Ensuring the security of remote sensing images is essential to prevent unauthorized access, tampering, and misuse. Deep learning-based digital watermarking offers a promising solution by embedding imperceptible information to protect data integrity. This paper proposes SIR-DCGAN, an attention-guided robust watermarking method for remote sensing image protection. It incorporates an IR-FFM feature fusion module to enhance feature reuse across different layers and an SE-AM attention mechanism to emphasize critical watermark features. Additionally, a noise simulation sub-network is introduced to improve resistance against common and combined attacks. The proposed method achieves high imperceptibility and robustness while maintaining low computational cost. Extensive experiments on both remote sensing and natural image datasets validate its effectiveness, with performance consistently surpassing existing approaches. These results demonstrate the practicality and reliability of SIR-DCGAN for secure image distribution and copyright protection. Full article

Tower bolts play a crucial role as connecting components in wind turbines and are of great interest for health monitoring systems. Non-contact monitoring techniques offer superior efficiency, convenience, and intelligence compared to contact-based methods. However, the precision and robustness of the non-contact monitoring process are significantly impacted by suboptimal lighting conditions within the wind turbine tower. To address this problem, this article proposes an automated detection method for the bolt detachment of wind turbines in low-light scenarios. The approach leverages the deep convolutional generative adversarial network (DCGAN) to expand and augment the small-sample bolt dataset. Transfer learning is then applied to train the Zero-DCE++ low-light enhancement model and the bolt defect detection model, with the experimental verification of the proposed method’s effectiveness. The results reveal that the deep convolutional generative adversarial network can generate realistic bolt images, thereby improving the quantity and quality of the dataset. Additionally, the Zero-DCE++ light enhancement model significantly increases the mean brightness of low-light images, resulting in a decrease in the error rate of defect detection from 31.08% to 2.36%. In addition, the model’s detection performance is affected by shooting angles and distances. Maintaining a shooting distance within 1.6 m and a shooting angle within 20° improves the reliability of the detection results. Full article

Gas polyethylene (PE) pipes have an become essential component of the urban gas pipeline network due to their long service life and corrosion resistance. To prevent safety incidents, regular monitoring of gas pipelines is crucial. Traditional inspection methods face significant challenges, including low efficiency, high costs, and limited applicability. Machine vision-based inspection methods have emerged as a key solution to these issues. Despite this, the method also encounters the problem of scarcity of defect samples and uneven data distribution in gas pipeline defect detection. For this reason, an improved Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. By integrating the Minibatch Discrimination (MD), Spectral Normalization (SN), Self-Attention Mechanism (SAM) and Two-Timescale Update Rule (TTUR), the proposed approach overcomes the original DCGAN’s limitations, including mode collapse, low resolution of generated images, and unstable training, the data augmentation of defective images inside the pipeline is realized. Experimental results demonstrate the superiority of the improved algorithm in terms of image generation quality and diversity, while the ablation study validates the positive impact of the improvement in each part. Additionally, the relationship between the number of augmented images and classification accuracy, showing that classifier performance improved across all scenarios when generated defect images were included. The findings indicate that the images produced by the improved model significantly enhance defect detection accuracy and hold considerable potential for practical application. Full article

Semantic communication technology, as it allows for source data meaning extraction and the transmission of appropriate semantic information only, has the potential to extend Shannon’s paradigm, which is concerned with the reproduction of a message from one location to another, regardless of its meaning. Nevertheless, some user terminals (UTs) may experience inadequate service due to their geolocation in reference to the base stations, which may entirely affect the accuracy of transmission and complicate deployment and implementation. A High-Altitude Platform Station (HAPS) serves as a key enabler for the deployment of wireless broadband in inaccessible areas, such as in coastal, desert, and mountainous areas. This paper proposes a novel HAPS relay-based semantic communication scheme, named DeepSTAS, which leverages deep learning techniques to enhance transmission accuracy. The proposed scheme focuses on attention-based semantic signal decoding, denoising, and forwarding modes; thus, called a CSA-DCGAN SDF HAPS relay network. The simulation results reveal that the proposed system with attention mechanisms significantly outperforms the system without attention mechanisms, both in peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index (MS-SSIM); the proposed system can achieve a 2 dB gain when leveraging the attention mechanisms, and a PSNR of 38.5 dB can be obtained, with an MS-SSIM exceeding 0.999 at an approximate SNR of only 20 dB. The system provides considerable performance, more than 37 dB, and a corresponding MS-SSIM close to 0.999 at an estimated SNR of 20 dB when the CIFAR-100 dataset is considered and an MS-SSIM of 0.965 at an approximate SNR of only 10 dB on the Kodak dataset. The proposed system holds promise to maintain consistent performance even at low SNRs across various channel conditions. Full article

Accurate, rapid, and automatic seafloor sediment classification represents a crucial challenge in marine sediment research. To address this, our study proposes a seafloor sediment classification method integrating convolutional neural networks (CNNs) with small-sample multi-beam backscatter data. We implemented four CNN architectures for classification—LeNet, AlexNet, GoogLeNet, and VGG—all achieving an overall accuracy exceeding 92%. To overcome the scarcity of seafloor sediment acoustic image data, we applied a deep convolutional generative adversarial network (DCGAN) for data augmentation, incorporating a de-normalization and anti-normalization module into the original DCGAN framework. Through comparative analysis of the generated versus original datasets using visual inspection and grayscale co-occurrence matrix methods, we substantially enhanced the similarity between synthetic and authentic images. Subsequent model training using the augmented dataset demonstrated improved classification performance across all architectures: LeNet showed a 1.88% accuracy increase, AlexNet an increase of 1.06%, GoogLeNet an increase of 2.59%, and VGG16 achieved a 2.97% improvement. Full article