EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss

The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by neural network methods, such as the generative adversarial network (GAN), which is a powerful generative network. Among many different types of GAN, the proposed method is performed using the Wasserstein generative adversarial network with gradient penalty (WGANGP). Since edge information is the most important factor in an image, the style loss function is applied to represent the perceptual information of the edge in order to preserve small edge information and capture its perceptual similarity. As a result, the proposed method improves the similarity between sharp and blurred images by minimizing the Wasserstein distance, and it captures well the perceptual similarity using the style loss function, considering the correlation of features in the convolutional neural network (CNN). To confirm the performance of the proposed method, three experiments are conducted using two datasets: the GOPRO Large and Kohler dataset. The optimal solutions are found by changing the parameter values experimentally. Consequently, the experiments depict that the proposed method achieves 0.98 higher performance in structural similarity (SSIM) and outperforms other de-blurring methods in the case of both datasets.

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Perceptual Fusion of Electronic Chart and Marine Radar Image

Journal of Marine Science and Engineering ◽

10.3390/jmse9111245 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1245

Author(s):

Chuang Zhang ◽

Meihan Fang ◽

Chunyu Yang ◽

Renhai Yu ◽

Tieshan Li

Keyword(s):

Low Frequency ◽

Wasserstein Distance ◽

Radar Image ◽

Navigation Systems ◽

Generative Adversarial Network ◽

Registration Method ◽

Noise Interference ◽

Adversarial Network ◽

Fast Training ◽

High Consistency

Electronic charts and marine radars are indispensable equipment in ship navigation systems, and the fusion display of these two parts ensures that the vessel can display dangerous moving targets and various obstacles on the sea. To reduce the noise interference caused by external factors and hardware, a novel radar image denoising algorithm using the concept of Generative Adversarial Network (GAN) using Wasserstein distance is proposed. GAN focuses on transferring the image noise distribution between strong and weak noise, while the perceptual loss approach is to suppress the noise by comparing the perceptual characteristics of the output after denoising. Afterwards, an image registration method based on image transformation is proposed to eliminate the imaging difference between the radar image and chart image, in which the visual attribute transfer approach is used to transform images. Finally, the sparse theory is used to process the high frequency and low frequency subband coefficients of the detection image obtained by the fast Fourier transform in parallel to realizing the image fusion. The results show that the fused contour has a high consistency, fast training speed and short registration time.

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Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning

International Journal of Aerospace Engineering ◽

10.1155/2021/6099818 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Gang Xiang ◽

Kun Tian

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Working Conditions ◽

Manufacturing Industry ◽

Probability Distributions ◽

Wasserstein Distance ◽

Target Domain ◽

Target Feature ◽

Generative Adversarial Network ◽

Adversarial Network

In recent years, deep learning methods which promote the accuracy and efficiency of fault diagnosis task without any extra requirement of artificial feature extraction have elicited the attention of researchers in the field of manufacturing industry as well as aerospace. However, the problems that data in source and target domains usually have different probability distributions because of different working conditions and there are insufficient labeled or even unlabeled data in target domain significantly deteriorate the performance and generalization of deep fault diagnosis models. To address these problems, we propose a novel Wasserstein Generative Adversarial Network with Gradient Penalty- (WGAN-GP-) based deep adversarial transfer learning (WDATL) model in this study, which exploits a domain critic to learn domain invariant feature representations by minimizing the Wasserstein distance between the source and target feature distributions through adversarial training. Moreover, an improved one-dimensional convolutional neural network- (CNN-) based feature extractor which utilizes exponential linear units (ELU) as activation functions and wide kernels is designed to automatically extract the latent features of raw time-series input data. Then, the fault model classifier trained in one working condition (source domain) with sufficient labeled samples could be generalized to diagnose data in other working conditions (target domain) with insufficient labeled samples. Experiments on two open datasets demonstrate that our proposed WDATL model outperforms most of the state-of-the-art approaches on transfer diagnosis tasks under diverse working circumstances.

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Image Denoising Using an Improved Generative Adversarial Network with Wasserstein Distance

10.23919/ccc52363.2021.9550033 ◽

2021 ◽

Author(s):

Qian Wang ◽

Han Liu ◽

Guo Xie ◽

Youmin Zhang

Keyword(s):

Image Denoising ◽

Wasserstein Distance ◽

Generative Adversarial Network ◽

Adversarial Network

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Intraoral Image Generation by Progressive Growing of Generative Adversarial Network and Evaluation of Generated Image Quality by Dentists

10.21203/rs.3.rs-117942/v1 ◽

2020 ◽

Author(s):

Kazuma Kokomoto ◽

Rena Okawa ◽

Kazuhiko Nakano ◽

Kazunori Nozaki

Keyword(s):

Private Information ◽

Data Augmentation ◽

Wasserstein Distance ◽

Training Data ◽

Image Generation ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Pediatric Dentists ◽

Realistic Images ◽

Clinical Cases

Abstract Dentists need experience with plenty of clinical cases to practice specialized skills. However, the need to protect patients’ private information limits the ability to utilize lots of intraoral images obtained from clinical cases. In this study, since generating realistic images could making utilizing lots of intraoral images possible, intraoral images are generated by using a progressive growing of generative adversarial network. 35,254 intraoral images were used as training data with resolutions of 128×128, 256×256, 512×512, and 1,024×1,024. The results of training datasets with and without data augmentation were compared. The sliced Wasserstein distance (SWD) was calculated to evaluate the generated images. Next, 50 real images and 50 generated images for each resolution were randomly selected and shuffled. Twelve pediatric dentists were asked to observe these images and assess whether each was real or generated. The accuracy of the assessment of the 1,024×1,024 images was significantly higher than that of the other resolutions. In conclusion, generated intraoral images with resolutions of 512×512 or lower were so realistic that the dentists could not distinguish whether they were real or generated. This implies that generated images can be used for dental education or data augmentation for deep learning free from privacy restrictions.

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4-Class MI-EEG Signal Generation and Recognition with CVAE-GAN

Applied Sciences ◽

10.3390/app11041798 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1798

Author(s):

Jun Yang ◽

Huijuan Yu ◽

Tao Shen ◽

Yaolian Song ◽

Zhuangfei Chen

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Feature Matching ◽

Continuous Optimization ◽

Training Data ◽

Small Scale ◽

Eeg Signal ◽

Generative Adversarial Network ◽

The Real ◽

Adversarial Network

As the capability of an electroencephalogram’s (EEG) measurement of the real-time electrodynamics of the human brain is known to all, signal processing techniques, particularly deep learning, could either provide a novel solution for learning but also optimize robust representations from EEG signals. Considering the limited data collection and inadequate concentration of during subjects testing, it becomes essential to obtain sufficient training data and useful features with a potential end-user of a brain–computer interface (BCI) system. In this paper, we combined a conditional variational auto-encoder network (CVAE) with a generative adversarial network (GAN) for learning latent representations from EEG brain signals. By updating the fine-tuned parameter fed into the resulting generative model, we could synthetize the EEG signal under a specific category. We employed an encoder network to obtain the distributed samples of the EEG signal, and applied an adversarial learning mechanism to continuous optimization of the parameters of the generator, discriminator and classifier. The CVAE was adopted to adjust the synthetics more approximately to the real sample class. Finally, we demonstrated our approach take advantages of both statistic and feature matching to make the training process converge faster and more stable and address the problem of small-scale datasets in deep learning applications for motor imagery tasks through data augmentation. The augmented training datasets produced by our proposed CVAE-GAN method significantly enhance the performance of MI-EEG recognition.

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Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists

Scientific Reports ◽

10.1038/s41598-021-98043-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kazuma Kokomoto ◽

Rena Okawa ◽

Kazuhiko Nakano ◽

Kazunori Nozaki

Keyword(s):

Private Information ◽

Data Augmentation ◽

Wasserstein Distance ◽

Training Data ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Pediatric Dentists ◽

Clinical Cases

AbstractDentists need experience with clinical cases to practice specialized skills. However, the need to protect patient's private information limits their ability to utilize intraoral images obtained from clinical cases. In this study, since generating realistic images could make it possible to utilize intraoral images, progressive growing of generative adversarial networks are used to generate intraoral images. A total of 35,254 intraoral images were used as training data with resolutions of 128 × 128, 256 × 256, 512 × 512, and 1024 × 1024. The results of the training datasets with and without data augmentation were compared. The Sliced Wasserstein Distance was calculated to evaluate the generated images. Next, 50 real images and 50 generated images for each resolution were randomly selected and shuffled. 12 pediatric dentists were asked to observe these images and assess whether they were real or generated. The d prime of the 1024 × 1024 images was significantly higher than that of the other resolutions. In conclusion, generated intraoral images with resolutions of 512 × 512 or lower were so realistic that the dentists could not distinguish whether they were real or generated. This implies that the generated images can be used in dental education or data augmentation for deep learning, without privacy restrictions.

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ESR-GAN: Environmental Signal Reconstruction Learning With Generative Adversarial Network

IEEE Internet of Things Journal ◽

10.1109/jiot.2020.3018621 ◽

2021 ◽

Vol 8 (1) ◽

pp. 636-646

Author(s):

Xu Kang ◽

Liang Liu ◽

Huadong Ma

Keyword(s):

Signal Reconstruction ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Environmental Signal

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