Automotive Radar Interference Mitigation Based on a Generative Adversarial Network

In this paper, a generative adversarial network based on SMPL model is used to reconstruct three-dimensional (3D) human mesh from a single image. The generative adversarial network consists of two parts (generative network and adversarial network). The generative network is made up of a deep residual network and a multilayer perception network. The goal is to generate suitable SMPL model parameters and camera parameters which fit the 2D and 3D position annotation of human skeleton joints. The adversarial network judges whether the shape and pose parameters generated by the generative network match the real human body through a discriminator. The purpose is to guide the generative network output parameters that conform to the laws of human kinematics. A geometric prior of the 3D joint angle limitation of the human body is introduced into the network to further constrain the generated human pose. Experiments verify the effectiveness of the algorithm.

The terahertz (THz) metamaterial sensor design is typically complex and requires substantial expertise in physics. To simplify this process, we propose a novel reverse design model based on an improved conditional generative adversarial network that integrates self-attention generative adversarial network and Wasserstein generative adversarial network (WGAN) networks, and is referred to as the self-attention conditional Wasserstein GAN (SACW-GAN) model. By using the target response of the sensor as the input to the generator network, and incorporating labeling information, an attention mechanism, and the Wasserstein distance, we achieve effective reverse design of THz metamaterial sensors. The simulation results demonstrate the model's high performance, with spectral and image accuracies of 95% and 97%, respectively. This deep learning approach offers new perspectives and methodologies for the reverse design and application of THz metamaterial sensors, significantly advancing the field.

Generative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

Aiming at the problems of low signal-to-noise ratio, low resolution and low illumination in low illumination environment, this paper proposes a low illumination images enhancement method based on generative adversarial networks with self attention mechanism. Firstly, the DenseNet framework is used to build the generator network, and the self attention mechanism module and constraint conditions are introduced. At the same time, the average discriminator is used to improve the traditional binary discriminator. Secondly, the low illumination images is introduced into the generator network to generate the illumination enhancement images, and then the discriminator network is used to supervise the enhancement effect of the generator on the low illumination images. Through the game between the generator and the discriminator, the network weight is continuously optimized, and finally the generator has better enhancement effect on the low illumination images. Experimental results show that, compared with the existing mainstream methods, this method not only has obvious advantages in brightness enhancement and clarity restoration of low illumination images, but also has significant advantages in objective evaluation indexes of images quality such as peak signal-to-noise ratio and structural similarity.KeywordsLow illumination images enhancementConditional generative adversarial networksDenseNetSelf attention mechanismAverage discriminator

Generative adversarial networks (GANs) with gaming abilities have been widely applied in image generation. However, gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying scenes. Enhancing the relation of hierarchical information in a generation network and enlarging differences of different network architectures can facilitate more structural information to improve the generation effect for image generation. In this paper, we propose an enhanced GAN via improving a generator for image generation (EIGGAN). EIGGAN applies a spatial attention to a generator to extract salient information to enhance the truthfulness of the generated images. Taking into relation the context account, parallel residual operations are fused into a generation network to extract more structural information from the different layers. Finally, a mixed loss function in a GAN is exploited to make a tradeoff between speed and accuracy to generate more realistic images. Experimental results show that the proposed method is superior to popular methods, i.e., Wasserstein GAN with gradient penalty (WGAN-GP) in terms of many indexes, i.e., Frechet Inception Distance, Learned Perceptual Image Patch Similarity, Multi-Scale Structural Similarity Index Measure, Kernel Inception Distance, Number of Statistically-Different Bins, Inception Score and some visual images for image generation.

Segmentation of breast cancer tumor plays an important role in identifying the location of the tumor, to know the shape of tumor and hence the stage of breast cancer. This paper deals with the segmentation of tumor from whole mammographic mass images using Generative Adversarial Network (GAN). A mini dataset was considered with mammograms and their corresponding ground truth images. Pre-processing like image format conversion, enhancement, pectoral muscle removal and resizing was performed on raw mammogram images. GANs have two neural nets called generative and discriminative networks that compete against each other to obtain the segmentation output. PIX2PIX is a conditional GAN variant which has U-Net as the Generator network and a simple deep neural net as the discriminator. The input to the network was pair of pre-processed mass image and the associated ground truth. A binary image with highlighted tumor was obtained as output. The performance of GAN was evaluated by plotting Generator and discriminator loss. The segmented output was compared with corresponding ground truth. Metrics like Jaccard index, Jaccard distance and Dice-coefficient were calculated. A Dice-coefficient and Jaccard index of 90% and 88.38% was achieved. In future, higher accuracy could be achieved by involving larger dataset to make the system robust.

Many machine learning models require huge datasets to get trained, to make correct predictions, or to increase their accuracy, that's where generative adversarial network (GAN) comes into the picture. GAN is a deep learning model, which can be used to create artificial data. It consists of two neural networks that cooperate in a way that resembles a game: a generator network and a discriminator network. Within the suggested model in this work, a dense motion network-based first-order motion model for image animation is developed. Here a trained GAN extracts the face landmarks from the driving video and develops the embedding model to create the synthesis video using the dedicated module to prepare the Deepfakes, employing key point detectors as a baseline. Lastly, an approach that makes use of dense motion networks to increase the effectiveness of a collection of GAN generators is provided. With the help of the sequel driving combination of driving video with the source image, the given results produce the augmented animation video. Hence, it is tried to implement a lighter model which consists of modules, with the approximate same accuracy when compared with other implementations of GAN. This work has a wide range of applications, including doubling dataset counts with a small number of sources, creating real-time backgrounds and characters for the gaming and animation industries using CG platforms, translating clothes, predicting videos, creating 3D objects, etc.

Compared with supervised learning, unsupervised learning is more practical; however, the associated training process is more difficult and complex. To solve the problems of unstable training and insufficient diversity of generative adversarial networks (GAN), which are widely used to realize unsupervised learning, we propose a novel unsupervised remote sensing image super-resolution method based on a reverse generating network module and the adaptive domain distance measurement network. The discriminant network of GAN is considered as a tool to measure a certain image attribute instead of the original GAN binary classification network. Furthermore, the adaptive domain distance measurement network is used to back feed the information of a high-resolution image to guide the optimization of the generating network. The results of experiments performed on various datasets demonstrate the effectiveness of the proposed method.

Due to the real working conditions, the collected mechanical fault datasets are actually limited and always highly imbalanced, which restricts the diagnosis accuracy and stability. To solve these problems, we present an imbalanced fault diagnosis method based on the generative model of conditional-deep convolutional generative adversarial network (C-DCGAN) and provide a study in detail. Deep convolutional generative adversarial network (DCGAN), based on traditional generative adversarial networks (GAN), introduces convolutional neural network into the training for unsupervised learning to improve the effect of generative networks. Conditional generative adversarial network (CGAN) is a conditional model obtained through introducing conditional extension into GAN. C-DCGAN is a combination of DCGAN and CGAN. In C-DCGAN, based on the feature extraction ability of convolutional networks, through the structural optimization, conditional auxiliary generative samples are used as augmented data and applied in machine fault diagnosis. Two datasets (Bearing dataset and Planetary gear box dataset) are carried out to validate. The simulation experiments showed that the improved performance is mainly due to the generated signals from C-DCGAN to balance the dataset. The proposed method can deal with imbalanced fault classification problem much more effectively. This model could improve the accuracy of fault diagnosis and the generalization ability of the classifier in the case of small samples and display better fault diagnosis performance.

Recovering images from highly undersampled measurements has a wide range of applications across imaging sciences. State-of-the-art analytics however are not aware of the image perceptual quality, and demand iterative algorithms that incur significant computational overhead. To sidestep these hurdles, this paper brings forth a novel compressive imaging framework using deep neural networks that approximates a low-dimensional manifold of images using generative adversarial networks. To ensure the images are consistent with the measurements a recurrent GAN (RGAN) architecture is deployed that consists of multiple alternative blocks of generator networks and affine projection, which is then followed by a discriminator network to score the perceptual quality of the generated images. A deep residual network with skip connections is used for the generator, while the discriminator is a multilayer perceptron. Experiments performed with real-world contrast enhanced MRI data corroborate the diagnostic quality of the retrieved images relative to state-of-the-art CS schemes. In addition, it achieves about two-orders of magnitude faster reconstruction.

The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and in particular Deep Learning (DL) has become one of the most popular image classification algorithms producing results comparable to and in some cases superior to human experts. These kind of algorithms need the input images used for the training to be labeled, and even if there is a large amount of images most of them are not labeled and it takes structural engineers a large amount of time to do it. The current data earthquakes image data bases do not contain the label information or is incomplete slowing significantly the advance of a solution and are incredible difficult to search. To be able to train a ML algorithm to classify one of the structural damages it took the architecture school an entire year to gather 200 images of the specific damage. That number is clearly not enough to avoid overfitting so for this thesis we decided to generate synthetic images for the specific structural damage. In particular we attempt to use Generative Adversarial Neural Networks (GANs) to generate the synthetic images and enable the fast classification of rail and road damage caused by earthquakes. Fast classification of rail and road damage can allow for the safety of people and to better prepare the reconnaissance teams that manage recovery tasks. GANs combine classification neural networks with generative neural networks. For this thesis we will be combining a convolutional neural network (CNN) with a generative neural network. By taking a classifier trained in a GAN and modifying it to classify other images the classifier can take advantage of the GAN training without having to find more training data. The classifier trained in this way was able to achieve an 88\% accuracy score when classifying images of structural damage caused by earthquakes.

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as “Real” or “Fake” respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

<span lang="EN-IN">There is a growing need for an image encryption scheme, for huge amount of social media data or even the medical data to secure the privacy of the patients or the user. This study introduces a ground-breaking deep learning architecture named crypto generative adversarial networks (CryptoGAN), a novel architecture for generating cipher images. This architecture has the ability to generate both encrypted and decrypted images. The CryptoGAN system consists of an initial encryption network, a generative network that verifies the output against the desired domain, and a subsequent decryption phase. The generative adversarial networks (GAN) are utilised as the learning network to generate cipher images. This is achieved by training the neural network using images encrypted from a conventional image encryption scheme such as advanced encryption standards (AES), and learning from the resulting losses. This enhances security measures when dealing with a large dataset of photos. The assessment of the performance metrics of the encrypted image, including entropy, histogram, correlation plot, and vulnerability to assaults, demonstrates that the suggested generative network may get a higher level of security.</span>

Retinal vessel segmentation is of great significance for assisting doctors in diagnosis of ophthalmological diseases such as diabetic retinopathy, macular degeneration and glaucoma. This article proposes a new retinal vessel segmentation algorithm using generative adversarial learning with a large receptive field. A generative network maps an input retinal fundus image to a realistic vessel image while a discriminative network differentiates between images drawn from the database and the generative network. Firstly, the proposed generative network combines shallow features with the upsampled deep features to assemble a more precise vessel image. Secondly, the residual module in the proposed generative and discriminative networks can effectively help deep nets easy to optimize. Moreover, the dilated convolutions in the proposed generative network effectively enlarge the receptive field without increasing the amount of computations. A number of experiments are conducted on two publicly available datasets (DRIVE and STARE) achieving the segmentation accuracy rates of 95.63% and 96.84%, and the average areas under the ROC curve of 98.12% and 98.53%. Performance results show that the proposed automatic retinal vessel segmentation algorithm outperforms state‐of‐the‐art algorithms in many validation metrics. The proposed algorithm can not only detect small tiny blood vessels but also capture large‐scale high‐level semantic vessel features.

The goal of speech super-resolution (SSR) or speech bandwidth expansion is to generate the missing high-frequency components for a given low-resolution speech signal. It has the potential to improve the quality of telecommunications. We propose a new method for SSR that leverages the generative adversarial networks (GANs) and a regularization method for stabilizing the GAN training. The generator network is a convolutional autoencoder with 1D convolution kernels, operating along time-axis and generating the high-frequency log-power spectra from the low-frequency log-power spectra input. We employ two recent deep neural network (DNN) based approaches to compare them with our proposed method, including both objective speech quality metrics and subjective perceptual tests. We show that our proposed method outperforms the baseline methods in terms of both objective and subjective evaluations.