Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN
Complexity, 2021 Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time ...
Jianghua Nie +3 more
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Mechanical and electrical equipment fault diagnosis based on dual attention mechanism and S-BiGAN
Hangkong gongcheng jinzhan, 2023 The accurate fault diagnosis of mechanical and electrical equipment under the condition of limited label samples is of great significance for improving the health management ability of complex mechanical and electrical equipment.
JIAO Xiaoxuan +4 more
doaj +1 more source
DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer [PDF]
IEEE International Conference on Computer Vision, 2021 The paper proposes a Dynamic ResBlock Generative Adversarial Network (DRB-GAN) for artistic style transfer. The style code is modeled as the shared parameters for Dynamic ResBlocks connecting both the style encoding network and the style transfer network.
Wenju Xu +3 more
semanticscholar +1 more source
Image De-Raining Using a Conditional Generative Adversarial Network [PDF]
IEEE transactions on circuits and systems for video technology (Print), 2017 Severe weather conditions, such as rain and snow, adversely affect the visual quality of images captured under such conditions, thus rendering them useless for further usage and sharing.
He Zhang +2 more
semanticscholar +1 more source
High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative adversarial network [PDF]
The Physics of Fluids, 2021 In this study, a deep learning-based approach is applied with the aim of reconstructing high-resolution turbulent flow fields using minimal flow fields data. A multi-scale enhanced super-resolution generative adversarial network with a physics-based loss
M. Yousif, Linqi Yu, Heechang Lim
semanticscholar +1 more source
Attentive Generative Adversarial Network for Raindrop Removal from A Single Image [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Raindrops adhered to a glass window or camera lens can severely hamper the visibility of a background scene and degrade an image considerably. In this paper, we address the problem by visually removing raindrops, and thus transforming a raindrop degraded
Rui Qian +4 more
semanticscholar +1 more source
An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks
IEEE Internet of Things Journal, 2023 As communication technology advances, various and heterogeneous data are communicated in distributed environments through network systems. Meanwhile, along with the development of communication technology, the attack surface has expanded, and concerns ...
Cheolhee Park +5 more
semanticscholar +1 more source
BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising
IEEE Access, 2021 Deep convolutional neural networks and generative adversarial networks currently attracted the attention of researchers because it is more effective than conventional representation-based methods.
Duc My Vo +3 more
doaj +1 more source
Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss [PDF]
IEEE Transactions on Medical Imaging, 2017 The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient.
Qingsong Yang +9 more
semanticscholar +1 more source
A new generative adversarial network for medical images super resolution
Scientific Reports, 2022 For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution.
Waqar Ahmad +3 more
semanticscholar +1 more source