Enhanced CNN for image denoising

Enhanced CNN for image denoising

Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation....

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Bibliographic Details
Main Authors: Chunwei Tian, Yong Xu, Lunke Fei, Junqian Wang, Jie Wen, Nan Luo
Format: Article
Language:English
Published: Wiley 2019-01-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
image restoration
image representation
learning (artificial intelligence)
image denoising
neural nets
convolution
image restoration CNN
enhanced CNN
flexible architectures
deep convolutional neural networks
deep network architecture
Deeper networks
performance saturation
convolutional neural denoising network
residual learning
batch normalisation techniques
training difficulties
dilated convolutions
authors
Online Access:https://digital-library.theiet.org/content/journals/10.1049/trit.2018.1054

Internet

https://digital-library.theiet.org/content/journals/10.1049/trit.2018.1054

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