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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

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

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

Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction

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recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3552853

Abstract: This work presents a novel zero-shot learning method for undersampled magnetic resonance imaging (MRI) reconstruction. The proposed method utilizes a plug-and-play approach, wherein the denoiser neural network, serving as the image prior, is trained solely… read more here.

Keywords: plug play; reconstruction; magnetic resonance; zero shot ... See more keywords

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

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

Intelligent FCDAE Denoiser for Reliable Decoding Over Correlated Noise Channel

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recommendations! Published in 2024 at "IEEE Transactions on Vehicular Technology"

DOI: 10.1109/tvt.2024.3399838

Abstract: In practical systems, colored noise commonly imposes on the data delivered, since the ideal white noise can hardly be achieved. In order to suppress the noise, we propose a denoiser based on a fully convolutional… read more here.

Keywords: fcdae; intelligent fcdae; denoiser; fcdae denoiser ... See more keywords