基于复合卷积神经网络的图像去噪算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201702001

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Abstract According to the theory of deep learning, the process of image denoising can be regarded as a fitting process of a neural network. In this paper, an image denoising algorithm based on composite convolutional neural network is proposed through constructing a simple and efficient composite convolutional neural network. The first stage includes two convolutional neural networks with two layers. Some initial convolutional kernels of convolutional neural network with three layers in the second stage are trained by these two networks, respectively. The training time in the second stage is decreased and the robustness of the network is enhanced. Finally, the learned convolutional neural network in the second stage is applied to denoise a new image with noises. Experimental results show that the proposed algorithm is comparable to state of the art image denoising algorithms in peak signal to noise ratio(PNSR), structure similarity, and root mean square error(RMSE). Especially, when the noises get heavier, the proposed algorithm performs better with less training time.

Key words： Image Denoising Convolutional Neural Network Stochastic Gradient Descent Method

Received: 30 August 2016

ZTFLH:	TN 911.71
	TP 183

Fund:Supported by National Natural Science Foundation of China(No.61672477,61571410)

About author:: (Lü Yongbiao, born in 1992, master student. His research interests include machine learning and image processing.)(ZHAO Jianwei, born in 1977, Ph.D., professor. Her research interests include machine learning and image processing.)(CAO Feilong(Corresponding author), born in 1965, Ph.D., professor. His research interests include intelligence computing and image processing.)

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	Lü Yongbiao
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	CAO Feilong

Cite this article:

Lü Yongbiao,ZHAO Jianwei,CAO Feilong. Image Denoising Algorithm
Based on Composite Convolutional Neural Network[J]. , 2017, 30(2): 97-105.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201702001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I2/97

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