基于复合卷积神经网络的图像去噪算法  

Image Denoising Algorithm Based on Composite Convolutional Neural Network

作　　者：吕永标[1] 赵建伟[1] 曹飞龙[1] 

机构地区：[1]中国计量大学理学院应用数学系,杭州310018

出　　处：《模式识别与人工智能》2017年第2期97-105,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61672477;61571410)资助~~

摘　　要：基于深度学习理论,将图像去噪过程看成神经网络的拟合过程,构造简洁高效的复合卷积神经网络,提出基于复合卷积神经网络的图像去噪算法.算法第1阶段由2个2层的卷积网络构成,分别训练阶段2中的3层卷积网络中的部分初始卷积核,缩短阶段2中网络的训练时间和增强算法的鲁棒性.最后运用阶段2中的卷积网络对新的噪声图像进行有效去噪.实验表明文中算法在峰值信噪比、结构相识度及均方根误差指数上与当前较好的图像去噪算法相当,尤其当噪声加强时效果更佳且训练时间较短.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.

关 键 词：图像去噪 卷积神经网络 随机梯度下降法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]