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›› 2018,Vol. 33 ›› Issue (4): 838-848.

• Artificial Intelligence and Pattern Recognition •

### 基于块匹配三维滤波和卷积神经网络的图像去噪

Bei-Ji Zou1,2, Yun-Di Guo1,3, Qi He1,3, Ping-Bo Ouyang1,3, Ke Liu2, Zai-Liang Chen1,3,*

1. 1 School of Information Science and Engineering, Central South University, Changsha 410083, China;
2 Center for Information and Automation of China Nonferrous Metals Industry Association, Changsha 410011, China;
3 Center for Ophthalmic Imaging Research, Central South University, Changsha 410083, China
• 收稿日期:2017-06-22 修回日期:2018-01-26 出版日期:2018-07-05 发布日期:2018-07-05
• 通讯作者: Zai-Liang Chen,E-mail:zailiangchencs@gmail.com E-mail:zailiangchencs@gmail.com
• 作者简介:Bei-Ji Zou received his B.S. degree in computer software from Zhejiang University, Hangzhou, in 1982, and his M.S. and Ph.D. degrees in computer science and technology from Tsinghua University, Beijing, in 1984, and Hunan University, Changsha, in 2001, respectively. He joined the School of Computer and Communication at Hunan University, Changsha, in 1984, where he became an associate professor in 1997, and a professor in 2001. He served as the vice dean there since 1997. He is currently a professor and served as the dean of the School of Information Science and Engineering in Central South University, Changsha. His research interest is focused on computer graphics, image processing, and virtual reality technology. Until now he has published more than 100 papers in journals.
• 基金资助:

This research was supported by the National Natural Science Foundation of China under Grant Nos. 61573380 and 61672542, and Fundamental Research Funds for the Central Universities of China under Grant No. 2016zzts055.

### 3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising

Bei-Ji Zou1,2, Yun-Di Guo1,3, Qi He1,3, Ping-Bo Ouyang1,3, Ke Liu2, Zai-Liang Chen1,3,*

1. 1 School of Information Science and Engineering, Central South University, Changsha 410083, China;
2 Center for Information and Automation of China Nonferrous Metals Industry Association, Changsha 410011, China;
3 Center for Ophthalmic Imaging Research, Central South University, Changsha 410083, China
• Received:2017-06-22 Revised:2018-01-26 Online:2018-07-05 Published:2018-07-05
• Contact: Zai-Liang Chen,E-mail:zailiangchencs@gmail.com E-mail:zailiangchencs@gmail.com
• About author:Bei-Ji Zou received his B.S. degree in computer software from Zhejiang University, Hangzhou, in 1982, and his M.S. and Ph.D. degrees in computer science and technology from Tsinghua University, Beijing, in 1984, and Hunan University, Changsha, in 2001, respectively. He joined the School of Computer and Communication at Hunan University, Changsha, in 1984, where he became an associate professor in 1997, and a professor in 2001. He served as the vice dean there since 1997. He is currently a professor and served as the dean of the School of Information Science and Engineering in Central South University, Changsha. His research interest is focused on computer graphics, image processing, and virtual reality technology. Until now he has published more than 100 papers in journals.
• Supported by:

This research was supported by the National Natural Science Foundation of China under Grant Nos. 61573380 and 61672542, and Fundamental Research Funds for the Central Universities of China under Grant No. 2016zzts055.

Abstract: Block matching based 3D filtering methods have achieved great success in image denoising tasks. However, the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network (CNN) for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model (BMCNN) could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio (PSNR) when denoising images with high noise levels (σ > 40), and the best visual quality when denoising images with all the tested noise levels.

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