Results 41 to 50 of about 259,715 (355)
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising [PDF]
2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD), 2023 Low-count PET is an efficient way to reduce radiation and acquisition time, but the images often suffer from a low signal-to-noise ratio (SNR). Recent advances in deep learning have shown great potential in improving low-count PET image quality, but ...
Bo Zhou +12 more
semanticscholar +1 more source
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising [PDF]
IEEE Transactions on Image Processing, 2017 Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with ...
K. Zhang, W. Zuo, Lei Zhang
semanticscholar +1 more source
A Wavenet for Speech Denoising [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 In proceedings of the 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2018). Code: https://github.com/drethage/speech-denoising-wavenet - Audio examples: http://jordipons.me/apps/speech-denoising-wavenet/
Rethage, Dario +2 more
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Image Denoising: The Deep Learning Revolution and Beyond - A Survey Paper - [PDF]
SIAM Journal of Imaging Sciences, 2023 Image denoising (removal of additive white Gaussian noise from an image) is one of the oldest and most studied problems in image processing. An extensive work over several decades has led to thousands of papers on this subject, and to many well ...
Michael Elad +2 more
semanticscholar +1 more source
Denoising Adversarial Autoencoders [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space.
Antonia Creswell, Anil Anthony Bharath
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TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation [PDF]
arXiv.org, 2023 Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations.
David Berthelot +8 more
semanticscholar +1 more source
Dual Residual Attention Network for Image Denoising [PDF]
Pattern Recognition, 2023 In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e.
Wencong Wu +4 more
semanticscholar +1 more source
Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement
IEEE Access, 2020 This paper proposes an image denoising training framework based on Wasserstein Generative Adversarial Networks (WGAN) and applies it to cell image denoising. Cell image denoising is a challenging task which has high requirement on the recovery of feature
Songkui Chen +3 more
doaj +1 more source
Hyperspectral Imagery Denoising Using Minimum Noise Fraction and Video Non-Local Bayes Algorithms
Canadian Journal of Remote Sensing, 2022 Hyperspectral imagery (HSI) denoising is a popular research topic in remote sensing. In this paper, we propose a novel method for HSI denoising by performing Minimum Noise Fraction (MNF) to the original HSI data cube, thresholding the noisy output bands ...
Guang Yi Chen +2 more
doaj +1 more source
A denoising stacked autoencoders for transient electromagnetic signal denoising [PDF]
Nonlinear Processes in Geophysics, 2018 Abstract. Transient electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field signal(SFS) in TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information.
Xuben Wang +9 more
openaire +3 more sources