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MP-PCA denoising for diffusion MRS data: promises and pitfalls
NeuroImage, 2022 Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction
Jessie Mosso +5 more
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Hyperspectral Image Denoising Based on Multi-Stream Denoising Network [PDF]
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021 Hyperspectral images (HSIs) have been widely applied in many fields, such as military, agriculture, and environment monitoring. Nevertheless, HSIs commonly suffer from various types of noise during acquisition. Therefore, denoising is critical for HSI analysis and applications.
Feng Gao, Yan Gao, Junyu Dong
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Universal Framework for Joint Image Restoration and 3D Body Reconstruction
IEEE Access, 2021 Recent works have demonstrated excellent state-of-the-art achievements in image restoration and 3D body reconstruction from an input image. The 3D body reconstruction task, however, relies heavily on the input image’s quality.
Jonathan Samuel Lumentut +5 more
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Denoising of Image Gradients and Total Generalized Variation Denoising [PDF]
Journal of Mathematical Imaging and Vision, 2018 We revisit total variation denoising and study an augmented model where we assume that an estimate of the image gradient is available. We show that this increases the image reconstruction quality and derive that the resulting model resembles the total generalized variation denoising method, thus providing a new motivation for this model.
Birgit Komander +2 more
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Seismic Data Denoising Based on Sparse and Low-Rank Regularization
Energies, 2020 Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications.
Shu Li +4 more
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Cross-Scale KNN Image Transformer for Image Restoration
IEEE Access, 2023 Numerous image restoration approaches have been proposed based on attention mechanism, achieving superior performance to convolutional neural networks (CNNs) based counterparts.
Hunsang Lee +3 more
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Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain [PDF]
, 2013 In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-
Aelterman, Jan +4 more
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Video Denoising and Enhancement via Dynamic Video Layering
, 2017 Video denoising refers to the problem of removing "noise" from a video sequence. Here the term "noise" is used in a broad sense to refer to any corruption or outlier or interference that is not the quantity of interest.
Guo, Han, Vaswani, Namrata
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Blind Denoising Autoencoder [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 The final version accepted at IEEE Transactions on Neural Networks and Learning ...
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Research on adaptive modulus maxima selection of wavelet modulus maxima denoising
The Journal of Engineering, 2018 Microelectromechanical system (MEMS) accelerometers are small in size, low in power consumption and easily integrated. They can be used in intelligent hydraulic components to obtain the dynamic acceleration of the system and monitor the operating status ...
Wensi Ding, Zhiguo Li
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