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An Enhanced GAN for Image Generation

Computers, Materials & Continua 2024年第7期80卷 105-118页

作者： Chunwei Tian Haoyang Gao Pengwei Wang Bob Zhang PAMI Research Group University of MacaoMacao999078China School of Software Northwestern Polytechnical UniversityXi’an710129China Yangtze River Delta Research Institute Northwestern Polytechnical UniversityTaicang215400China Research&Development Institute Northwestern Polytechnical UniversityShenzhen518057China

Generative adversarial networks(GANs)with gaming abilities have been widely applied in image ***,gamistic generators and discriminators may reduce the robustness of the obtained GANs in image generation under varying *** 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 *** 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 *** into relation the context account,parallel residual operations are fused into a generation network to extract more structural information from the different ***,a mixed loss function in a GAN is exploited to make a tradeoff between speed and accuracy to generate more realistic *** 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.

关键词： Generative adversarial networks spatial attention mixed loss image generation

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A lightweight and effective deep learning model for Gaussian noise removal

A lightweight and effective deep learning model for Gaussian...

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第40届中国控制会议

作者： Dazi Li Wenjie Yu College of Information Science and Technology Beijing University of Chemical Technology

Presently,Gaussian noise removal is a very attractive research direction,because Gaussian noise can effectively simulate the real world noise when the real noise sources are complex and *** traditional denoising methods have high complexity and low *** of image Gaussian noise denoising will become more efficient with the development of deep *** this paper a novel deep learning model for Gaussian noise removal is *** proposed method combines dilated convolution with skip connection of residual learning,which is trained by our proposed mixed loss function during back *** only compared with existing traditional methods,but also compared with representative deep learning methods,our proposed model has a better *** addition,our proposed model has less layers and training parameters than representative deep learning *** several comparative experiments on test data sets,we can draw a conclusion that our proposed deep learning model can remove Gaussian noise more effectively than some state-of-art methods with quantitative and visual qualitative analyses.

关键词： deep learning dilated convolution multi-scale residual learning mixed loss

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