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An ELU Network with Total Variation for Image Denoising [article]

Tianyang Wang, Zhengrui Qin, Michelle Zhu
2017 arXiv   pre-print
We investigate the suitability by analyzing ELU's connection with trainable nonlinear reaction diffusion model (TNRD) and residual denoising.  ...  Finally, we conduct extensive experiments, showing that our model outperforms some recent and popular approaches on Gaussian denoising with specific or randomized noise levels for both gray and color images  ...  RReLU, and LReLU for denoising task in future work.  ... 
arXiv:1708.04317v1 fatcat:4rnawr453nfhnmoix6awug4eum

An ELU Network with Total Variation for Image Denoising [chapter]

Tianyang Wang, Zhengrui Qin, Michelle Zhu
2017 Lecture Notes in Computer Science  
We investigate the suitability by analyzing ELU's connection with trainable nonlinear reaction diffusion model (TNRD) and residual denoising.  ...  Finally, we conduct extensive experiments, showing that our model outperforms some recent and popular approaches on Gaussian denoising with specific or randomized noise levels for both gray and color images  ...  RReLU, and LReLU for denoising task in future work.  ... 
doi:10.1007/978-3-319-70090-8_24 fatcat:erhztjnfurd4vloj54bg3j64yi

FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy

Yifei Xu, Zhuming Zhou, Xiao Li, Nuo Zhang, Meizi Zhang, Pingping Wei, Changming Sun
2021 BioMed Research International  
Then, we integrate multiscale feature fusion (MSFF) block into the encoders which helps the network to learn multiscale features efficiently and enrich the information carried with skip connection and  ...  Firstly, the pooling layer in the network is replaced with a convolutional layer to reduce spatial loss of the fundus image.  ...  To address the above problems, we take measures before feeding the fundus images into our network, such as image cropping, image denoising, image enhancement [26] , image normalization, data augmentation  ... 
doi:10.1155/2021/6644071 pmid:33490274 pmcid:PMC7801055 fatcat:otngnb536bfgjffr5nkri7ryba

Joint Learning of Super-Resolution and Perceptual Image Enhancement for Single Image

Yifei Xu, Nuo Zhang, Li Li, Genan Sang, Yuewan Zhang, Zhengyang Wang, Pingping Wei
2021 IEEE Access  
In SRResNet [9] , stacked residual blocks and generative adversarial network are introduced to solve SR problem.  ...  RFA: The residual feature aggregation (RFA) groups the residual block together along with useful hierarchical features [11] .  ... 
doi:10.1109/access.2021.3068861 fatcat:el7xciykonbihhmpel3vge3uqy

Recent Advances in Convolutional Neural Networks [article]

Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen
2017 arXiv   pre-print
Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.  ...  Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged  ...  The ResNets in ResNets (RiR) paper [127] describes an architecture that merges classical convolutional networks and residual networks, where each block of RiR contains residual units and non-residual  ... 
arXiv:1512.07108v6 fatcat:rwmmwcy4ezd6pmt6scuaambd7m

Review: Deep Learning in Electron Microscopy [article]

Jeffrey M. Ede
2020 arXiv   pre-print
Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes.  ...  We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a creative commons 4.0 73 license.  ... 
arXiv:2009.08328v4 fatcat:umocfp5dgvfqzck4ontlflh5ca

Neural Computing [article]

Ayushe Gangal, Peeyush Kumar, Sunita Kumari, Aditya Kumar
2021 arXiv   pre-print
Different types of neural networks discovered so far and applications of some of those neural networks are focused on, apart from their theoretical understanding, the working and core concepts involved  ...  major researchers and innovators in this field and thus, encouraging the readers to develop newer and more advanced techniques for the same.  ...  Deep Residual Network A deep residual network, commonly known as a deep ResNet, created by (He, Zhang, Ren & Sun, 2015) , is a network with many layers.  ... 
arXiv:2107.02744v1 fatcat:kmfb6j3vcrby3mphgzwo6akho4

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative  ...  automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4591029 fatcat:zn2hvfyupvdwlnvsscdgswayci

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative  ...  automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4598227 fatcat:hm2ksetmsvf37adjjefmmbakvq

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative  ...  automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4399748 fatcat:63ggmnviczg6vlnqugbnrexsgy

A Survey on Human Cancer Categorization Based on Deep Learning

Ahmad Ibrahim, Hoda K. Mohamed, Ali Maher, Baochang Zhang
2022 Frontiers in Artificial Intelligence  
Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.  ...  Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification.  ...  The authors segmented the prostate with images of 3D MR through volumetric CN. To enable the volume-to-voltage prediction, the FCN was extended with residual blocks.  ... 
doi:10.3389/frai.2022.884749 pmid:35832207 pmcid:PMC9271903 fatcat:mzxsp7ui6bcklp6muo3n3yr4ae

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Waseem Rawat, Zenghui Wang
2017 Neural Computation  
, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012.  ...  Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s.  ...  RReLU Activations.  ... 
doi:10.1162/neco_a_00990 pmid:28599112 fatcat:ospodd7lbzhsbl4fkjzvpe6jgy

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative  ...  automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4413249 fatcat:35qbhenysfhvza2roihx52afuy

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative  ...  automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4429792 fatcat:qs6yuapx4vdbdmwna7ix7nnwty

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative  ...  automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4415407 fatcat:6dejwzzpmfegnfuktrld6zgpiq
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