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Block-Matching Convolutional Neural Network for Image Denoising
[article]
2017
arXiv
pre-print
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The denoised image is employed as a pilot signal for the block matching, and then denoising function for the block is learned by a CNN structure. ...
In this paper, we propose a block-matching convolutional neural network (BMCNN) method that combines NSS prior and CNN. ...
In this paper, a combined denoising framework named block-matching convolutional neural network (BMCNN) is presented. ...
arXiv:1704.00524v1
fatcat:l7dqbub7ujedjlzpbznc33t2wi
Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising
2018
IEEE Signal Processing Letters
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2018 pre-print arXiv:1803.02112v1 fatcat:k5yuhoumdnhfvatkm7plckaumaOther Versions
It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF) exploiting the mutual similarities between ...
We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. ...
The so-called block arXiv:1803.02112v1 [eess.IV] 6 Mar 2018 matching convolutional neural network (BMCNN) method [17] is similar, using groups of similar patches as inputs to a denoising CNN. ...
doi:10.1109/lsp.2018.2850222
fatcat:geqoko3slvhoxj3oamsc2vz25y
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network
[article]
2018
arXiv
pre-print
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Within this manuscript we propose the idea of denoising MRI Rician noise using a convolutional neural network. ...
The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. ...
This validates that the general applicability of the deep-learning based denoise model. In this paper, a deep convolutional neural network was proposed for image denoising. ...
arXiv:1712.08726v2
fatcat:qfhfdmxolzdwlh7pgplvgoczri
Patch Craft: Video Denoising by Deep Modeling and Patch Matching
[article]
2021
arXiv
pre-print
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With the emergence of convolutional neural networks (CNN), the patch-based framework has been abandoned. ...
Most CNN denoisers operate on the whole image, leveraging non-local relations only implicitly by using a large receptive field. ...
The first part of it, Temporal Filter 3D (Tf3D), is composed of T t blocks consisting of a 3D convolutions with 3 × 3 × 3 kernels followed by Leaky ReLUs (LReLU). ...
arXiv:2103.13767v2
fatcat:wij5h5xid5hode5ypznvtwa34q
Introducing Swish and Parallelized Blind Removal Improves the Performance of a Convolutional Neural Network in Denoising MR Images
2021
Magnetic Resonance in Medical Sciences
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To improve the performance of a denoising convolutional neural network (DnCNN) and to make it applicable to images with inhomogeneous noise, a refinement involving an activation function (AF) and an application ...
Improvements in the DnCNN were performed by three approaches. One is refinement of the AF of each neural network that constructs the DnCNN. ...
The authors would like to thank Canon Medical Systems Corp. for the use of clinical magnetic resonance images and brain-development org. for the use of the IXI Dataset. ...
doi:10.2463/mrms.mp.2020-0073
pmid:33583867
pmcid:PMC8922346
fatcat:34v4t47fvncmfpmb7tdouzctw4
Sparse Residual Learning of Deep Convolution Network for De-Noising Patch Based Block Match Three Dimension Algorithm
2018
International Journal for Research in Applied Science and Engineering Technology
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The de-noised image is employed as a basic estimate for the block matching, and then de-noising function for the block is learned by a DCNN structure. ...
This paper introduces a unique approach to de-noise an image based on concepts of Deep Convolution Neural Networks (DCNN) with sparse residual learning sparse reconstruction and batch normalization. ...
The best example is the Block Matching and 3D Filtering (BM3D) method [2] which is a very good in performance and highly engineered approach that made the state-of-the-art record in image de-noising ...
doi:10.22214/ijraset.2018.2067
fatcat:x4hnqvjqszgfbhpuklturj7bma
SAR Image Despeckling Based on Block-Matching and Noise-Referenced Deep Learning Method
2022
Remote Sensing
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The denoising network designed for this method is an encoder–decoder convolutional neural network and is accommodated to small image patches. ...
This method firstly constructs a large number of noisy pairs as training input by similarity-based block-matching in either one noisy SAR image or multiple images. ...
The latest developments of supervised deep learning denoising methods, such as denoising convolutional neural network (DnCNN) [1] and fast and flexible denoising convolutional neural network (FFDNet) ...
doi:10.3390/rs14040931
fatcat:clnp7xqoavhklal7ivoru324lq
Image-Based 3D Mesh Denoising Through a Block Matching 3D Convolutional Neural Network Filtering Approach
2020
Zenodo
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Motivated by the impressive results of image denoising by 3D transform-domain collaborative filtering (CF), we extend it to 3D mesh denoising. ...
Throughout the years, several works have been proposed for 3D mesh denoising. ...
Acknowledgements Research supported by the European Union research and innovation program Horizon 2020 Marie Sklodowska WARMEST :loW Altitude Remote sensing for the Monitoring of the state of cultural ...
doi:10.5281/zenodo.3754153
fatcat:yucaqb5d7fhuphkm4cuhisxhve
Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-Learning-based Denoising
2020
Magnetic Resonance in Medical Sciences
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Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. ...
The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering. ...
Images denoised using (B) Gaussian filter, (C) total variation (TV) denoising, (D) block-matching and 3D filtering (BM3D), and (E) the proposed method. (F) Ground truth images. ...
doi:10.2463/mrms.tn.2019-0081
pmid:32147643
pmcid:PMC7952209
fatcat:rgcwoaietvbudhvxsek2lua6ei
Real-Time Medical Video Denoising with Deep Learning: Application to Angiography
2018
International Journal of Applied Information Systems
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This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. ...
The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. ...
Acknowledgments This work was supported by the National Institutes of Health grant number T35DK104689 (NIDDK Medical Student Research Fellowship). ...
doi:10.5120/ijais2018451755
pmid:29877510
pmcid:PMC5985814
fatcat:2yylttimtzdipputad3z4owv44
Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition
2021
Sensors
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Results show that, in all cases, our proposed approach for quality balancing yields improved recognition performance, which is especially effective when involving SWIR images at a longer standoff. ...
Finally, inspired by the singular value decomposition (SVD) theory, the proposed deblurring model of SVDFace is succinct, efficient and interpretable in structure. ...
Acknowledgments: Special thanks to the CCF-IFAA Research Fund for greatly supporting the writing of the paper. To learn more about IFAA: www.ifaa.org.cn/en. Accessed on 25 March 2021. ...
doi:10.3390/s21072322
pmid:33810407
fatcat:ilnzjxsm2jb2tbce2tgdwubtke
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network
[article]
2018
arXiv
pre-print
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This article introduces a Contracting Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D configurations within the GAN framework for low-dose CT denoising. ...
Recently, deep-learning-based algorithms have achieved promising results in low-dose CT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN). ...
ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers and associate editor for their constructive comments and sug- ...
arXiv:1802.05656v2
fatcat:n22v5lcmerdnre6bgbtkoagk3y
Exploiting Temporal Attention Features for Effective Denoising in Videos
[article]
2020
arXiv
pre-print
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The Attention Block used in this paper usessoft attention to ranks the filters for better training. ...
It is different from image denoising due to the tem-poral aspects of video frames, and any image denoising approach appliedto videos will result in flickering. ...
Much recently, some research has been done on using kernel predicting convolutional neural networks for denoising [27, 36] . ...
arXiv:2008.02344v2
fatcat:f23xe36nkfhdxbalmkyjb2qodu
Image denoising algorithm of social network based on multifeature fusion
2022
Journal of Intelligent Systems
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Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient convolution neural structure of multifeature ...
fusion is constructed for image denoising. ...
Design the realization of network image denoising The convolution neural structure of social network is divided into convolution layer and pooling layer, and each convolution layer is connected with a ...
doi:10.1515/jisys-2022-0019
fatcat:usgpogguezaotpfdi5f7f6ziju
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images
[article]
2022
arXiv
pre-print
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We define a novel deep learning framework for the real-time denoising of ultrasound images. ...
Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and ...
Acknowledgements This research is carried out as part of an Industrial PhD project funded by CNR-IMATI and Esaote S.p.A. under the CNR-Confindustria agreement. ...
arXiv:2101.09122v2
fatcat:yqeanxoiybg4vkk2ldhwsjskri
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