NDLI: Chest X-ray image denoising method based on deep convolution neural network

Content Provider	IET Digital Library
Author	Jin, Yan Jiang, Xiao Ben Wei, Zhen kun Li, Yuan
Abstract	To improve the visual effect of chest X-ray images and reduce the noise interference in disease diagnosis based on the chest X-ray images, the authors proposed an image denoising model based on deep convolution neural network. They utilise batch normalisation to solve the problem of performance degradation due to the increase of neural network layers, and use residual learning of the distribution of noise in noisy X-ray images. Specifically, the depthwise separable convolution is used to accelerate the convergence speed of network model, shorten the training time, and improve accuracy of the model. Compared to the several popular or the state-of-the-art denoising algorithms, their extensive experiments demonstrate that their method can not only achieve better denoising effects, but also significantly reduce the complexity of the network and shorten the computation time.
Starting Page	1970
Ending Page	1978
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 11, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0241
Journal	IET Image Processing
Publisher Date	2019-07-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Chest X-ray Image Chest X-ray Image Denoising Method Computer Vision And Image Processing Technique Convolution Deep Convolution Neural Network Denoising Effects Depthwise Separable Convolution Diseases Image Denoising Image Denoising Model Knowledge Engineering Technique Learning in AI Network Model Neural Nets Neural Network Layer Noise Interference Noisy X-Ray Image Optical, Image And Video Signal Processing Shorten State-of-the-art Denoising Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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