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   Articles with "denoiser" as a keyword



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Utilization of an attentive map to preserve anatomical features for training convolutional neural network based low dose CT denoiser.

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recommendations! Published in 2023 at "Medical physics"

DOI: 10.1002/mp.16263

Abstract: BACKGROUND The purpose of a convolutional neural network (CNN)-based denoiser is to increase the diagnostic accuracy of low-dose computed tomography (LDCT) imaging. To increase diagnostic accuracy, there is a need for a method that reflects… read more here.

Keywords: neural network; denoiser; convolutional neural; attentive map ... See more keywords
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Efficient cascading of multi-domain image Gaussian noise filters

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recommendations! Published in 2019 at "Journal of Real-Time Image Processing"

DOI: 10.1007/s11554-019-00868-9

Abstract: Image denoising is a well explored but still an active research topic. The focus is usually on achieving higher numerical quality which is theoretically interesting, however, often the factor of computation cost is not considered.… read more here.

Keywords: denoiser; quality; multi domain; image ... See more keywords
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A Methodology to Train a Convolutional Neural Network-Based Low-Dose CT Denoiser With an Accurate Image Domain Noise Insertion Technique

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3198948

Abstract: To mitigate the public health risks associated with the increasing utilization of computed tomography (CT), it is desirable to implement a low-dose scanning protocol. However, low-dose CT produces poor image quality due to the increased… read more here.

Keywords: neural network; denoiser; methodology; convolutional neural ... See more keywords
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Efficient Residual Shrinkage CNN Denoiser Design for Intelligent Signal Processing: Modulation Recognition, Detection, and Decoding

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recommendations! Published in 2022 at "IEEE Journal on Selected Areas in Communications"

DOI: 10.1109/jsac.2021.3126074

Abstract: The noises embedded in signals will degrade the signal processing quality. Traditional denoising algorithms might not work in practical systems since the statistical characteristics of noises might not be learned. To address this issue, we… read more here.

Keywords: modulation recognition; denoiser; signal processing; detection decoding ... See more keywords

Provable Convergence of Plug-and-Play Priors With MMSE Denoisers

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recommendations! Published in 2020 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2020.3006390

Abstract: Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser. While PnP algorithms are well understood for denoisers performing maximum a posteriori probability (MAP) estimation, they have… read more here.

Keywords: mmse denoisers; plug play; denoiser; play priors ... See more keywords
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Deep Interactive Denoiser (DID) for X-Ray Computed Tomography

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recommendations! Published in 2021 at "IEEE Transactions on Medical Imaging"

DOI: 10.1109/tmi.2021.3101241

Abstract: Low-dose computed tomography (LDCT) is desirable for both diagnostic imaging and image-guided interventions. Denoisers are widely used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are becoming mainstream… read more here.

Keywords: denoiser; interactive denoiser; deep interactive; image ... See more keywords