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
doaj +1 more source
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
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
Spatial evolutionary generative adversarial networks [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2019 Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems.
Toutouh, Jamal +2 more
openaire +3 more sources
Generative adversarial network: An overview of theory and applications
Int. J. Inf. Manag. Data Insights, 2021 In recent times, image segmentation has been involving everywhere including disease diagnosis to autonomous vehicle driving. In computer vision, this image segmentation is one of the vital works and it is relatively complicated than other vision ...
Alankrita Aggarwal +2 more
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
Generative Adversarial Network in Medical Imaging: A Review [PDF]
Medical Image Anal., 2018 Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function.
Xin Yi, Ekta Walia, P. Babyn
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
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
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