scholarly journals Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks

2019 ◽  
Vol 127 (6-7) ◽  
pp. 845-862 ◽  
Author(s):  
He Zhang ◽  
Benjamin S. Riggan ◽  
Shuowen Hu ◽  
Nathaniel J. Short ◽  
Vishal M. Patel
Keyword(s):  
Generative Adversarial Networks ◽  
High Quality ◽  
Adversarial Networks

High-Quality Sonar Image Generation Algorithm Based on Generative Adversarial Networks

2021 ◽  
Author(s):  
Zhengyang Wang ◽  
Qingchang Guo ◽  
Min Lei ◽  
Shuxiang Guo ◽  
Xiufen Ye
Keyword(s):  
Generative Adversarial Networks ◽  
Image Generation ◽  
High Quality ◽  
Generation Algorithm ◽  
Sonar Image ◽  
Adversarial Networks

scholarly journals Smooth Deep Image Generator from Noises

2019 ◽  
Vol 33 ◽  
pp. 3731-3738 ◽  
Author(s):  
Tianyu Guo ◽  
Chang Xu ◽  
Boxin Shi ◽  
Chao Xu ◽  
Dacheng Tao
Keyword(s):  
Linear Interpolation ◽  
Natural Images ◽  
Generative Adversarial Networks ◽  
High Quality ◽  
Adversarial Networks ◽  
Deep Image ◽  
Image Generator ◽  
Input Perturbation ◽  
World Image ◽  
Strong Ability

Generative Adversarial Networks (GANs) have demonstrated a strong ability to fit complex distributions since they were presented, especially in the field of generating natural images. Linear interpolation in the noise space produces a continuously changing in the image space, which is an impressive property of GANs. However, there is no special consideration on this property in the objective function of GANs or its derived models. This paper analyzes the perturbation on the input of the generator and its influence on the generated images. A smooth generator is then developed by investigating the tolerable input perturbation. We further integrate this smooth generator with a gradient penalized discriminator, and design smooth GAN that generates stable and high-quality images. Experiments on real-world image datasets demonstrate the necessity of studying smooth generator and the effectiveness of the proposed algorithm.

scholarly journals Generative adversarial networks for generating synthetic features for Wi-Fi signal quality

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260308
Author(s):  
Mauro Castelli ◽  
Luca Manzoni ◽  
Tatiane Espindola ◽  
Aleš Popovič ◽  
Andrea De Lorenzo
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Signal Quality ◽  
Quality Service ◽  
High Quality ◽  
The Real ◽  
Adversarial Networks ◽  
High Quality Service

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and compared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.

scholarly journals Daydriex: Translating Nighttime Scenes towards Daytime Driving Experience at Night

2021 ◽  
Vol 11 (5) ◽  
pp. 2013
Author(s):  
Euihyeok Lee ◽  
Seungwoo Kang
Keyword(s):  
Input Image ◽  
Generative Adversarial Networks ◽  
User Perception ◽  
Missing Information ◽  
High Quality ◽  
Driving Experience ◽  
Processing Pipeline ◽  
Adversarial Networks ◽  
Image Translation ◽  
Translation Techniques

What if the window of our cars is a magic window, which transforms dark views outside of the window at night into bright ones as we can see in the daytime? To realize such a window, one of important requirements is that the stream of transformed images displayed on the window should be of high quality so that users perceive it as real scenes in the day. Although image-to-image translation techniques based on Generative Adversarial Networks (GANs) have been widely studied, night-to-day image translation is still a challenging task. In this paper, we propose Daydriex, a processing pipeline to generate enhanced daytime translation focusing on road views. Our key idea is to supplement the missing information in dark areas of input image frames by using existing daytime images corresponding to the input images from street view services. We present a detailed processing flow and address several issues to realize our idea. Our evaluation shows that the results by Daydriex achieves lower Fréchet Inception Distance (FID) scores and higher user perception scores compared to those by CycleGAN only.

scholarly journals Improving protein function prediction with synthetic feature samples created by generative adversarial networks

2019 ◽  
Author(s):  
Cen Wan ◽  
David T. Jones
Keyword(s):  
Protein Function ◽  
Data Augmentation ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Generative Adversarial Networks ◽  
Prediction Methods ◽  
High Quality ◽  
Adversarial Networks ◽  
Synthetic Protein ◽  
Protein Feature

AbstractProtein function prediction is a challenging but important task in bioinformatics. Many prediction methods have been developed, but are still limited by the bottleneck on training sample quantity. Therefore, it is valuable to develop a data augmentation method that can generate high-quality synthetic samples to further improve the accuracy of prediction methods. In this work, we propose a novel generative adversarial networks-based method, namely FFPred-GAN, to accurately learn the high-dimensional distributions of protein sequence-based biophysical features and also generate high-quality synthetic protein feature samples. The experimental results suggest that the synthetic protein feature samples are successful in improving the prediction accuracy for all three domains of the Gene Ontology through augmentation of the original training protein feature samples.

scholarly journals Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Liugang Gao ◽  
Kai Xie ◽  
Xiaojin Wu ◽  
Zhengda Lu ◽  
Chunying Li ◽  
...  
Keyword(s):  
Image Quality ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Dose Calculation ◽  
Cone Beam ◽  
Generative Adversarial Networks ◽  
High Quality ◽  
Passing Rates ◽  
Adversarial Networks ◽  
Synthetic Ct

Abstract Objective To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy. Methods The CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution. Results The image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy. Conclusions High-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.

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