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Block-Matching Convolutional Neural Network for Image Denoising [article]

Byeongyong Ahn, Nam Ik Cho
2017 arXiv   pre-print
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

Cristovao Cruz, Alessandro Foi, Vladimir Katkovnik, Karen Egiazarian
2018 IEEE Signal Processing Letters  
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]

Dongsheng Jiang, Weiqiang Dou, Luc Vosters, Xiayu Xu, Yue Sun, Tao Tan
2018 arXiv   pre-print
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]

Gregory Vaksman, Michael Elad, Peyman Milanfar
2021 arXiv   pre-print
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

Taro Sugai, Kohei Takano, Shohei Ouchi, Satoshi Ito
2021 Magnetic Resonance in Medical Sciences  
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

Kamalakshi N
2018 International Journal for Research in Applied Science and Engineering Technology  
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

Chen Wang, Zhixiang Yin, Xiaoshuang Ma, Zhutao Yang
2022 Remote Sensing  
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

Gerasimos Arvanitis, Aris Lalos, Konstantinos Moustakas
2020 Zenodo  
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

Motohide Kawamura, Daiki Tamada, Satoshi Funayama, Marie-Luise Kromrey, Shintaro Ichikawa, Hiroshi Onishi, Utaroh Motosugi
2020 Magnetic Resonance in Medical Sciences  
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

Praneeth Sadda, Taha Qarni
2018 International Journal of Applied Information Systems  
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

Zhicheng Cao, Xi Cen, Heng Zhao, Liaojun Pang
2021 Sensors  
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]

Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang
2018 arXiv   pre-print
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]

Aryansh Omray and Samyak Jain and Utsav Krishnan and Pratik Chattopadhyay
2020 arXiv   pre-print
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

Lanfei Zhao, Qidan Zhu
2022 Journal of Intelligent Systems  
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]

Simone Cammarasana, Paolo Nicolardi, Giuseppe Patanè
2022 arXiv   pre-print
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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