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[IEEE 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) - Shenzhen, China (2018.10.25-2018.10.27)] 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) - RDS-Denoiser: a Detail-preserving Convolutional Neural Network for Image Denoising
摘要： Image noise is usually modeled as additive independent Gaussian random variables with fixed standard deviation, and most existing methods developed under this assumption have difficulties handling spatially varying noise. In this work, we aim to solve the problem of image denoising when the noise level is unknown. We propose a simple yet effective Stacked Denoising Networks. It decomposes the denoising process into two stages. The Stage-I Denoising is to predict the noise map of noisy image. The Stage-II Denoising to further improve the visual quality and alleviate overfitting to Gaussian noise. Experiments show that RDS-Denoiser achieves competitive performance comparing to state-of-the-art denoising methods. In addition, we propose RDS-GAN, a conditional generative adversarial network, to further improve the visual quality and alleviate overfitting to Gaussian noise.
关键词： Image Denoising,Convolutional Neural Networks,Conditional Generative Adversarial Networks
更新于2025-09-19 17:15:36
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - An improvement on the CNN-based OAM Demodulator via Conditional Generative Adversarial Networks
摘要： In the paper, an Orbital Angular Momentum (OAM) demodulation method based on Conditional Generative Adversarial Networks(CGAN) is proposed to improve the accuracy of Convolutional Neural Networks (CNN) based demodulator. We train a CGAN on a limited data set, and the discriminator in CGAN is fine-tuned as a new classifier for OAM demodulation. Our numerical simulations demonstrate that the proposed method can improve the accuracy of OAM demodulator from 93.56% to 98.36% over 400-m free-space link when the turbulence strength equals 4×10-13m-2/3.
关键词： Deep Learning,OAM demodulation,Conditional Generative Adversarial Networks
更新于2025-09-16 10:30:52