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

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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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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Bei-Ji Zou, Yun-Di Guo, Qi He, Ping-Bo Ouyang & Zai-Liang Chen

  2. Center for Information and Automation of China Nonferrous Metals Industry Association, Changsha, 410011, China

    Bei-Ji Zou & Ke Liu

  3. Center for Ophthalmic Imaging Research, Central South University, Changsha, 410083, China

    Yun-Di Guo, Qi He, Ping-Bo Ouyang & Zai-Liang Chen

Authors
  1. Bei-Ji Zou
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  2. Yun-Di Guo
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  3. Qi He
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  4. Ping-Bo Ouyang
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  5. Ke Liu
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  6. Zai-Liang Chen
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Correspondence to Zai-Liang Chen.

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Zou, BJ., Guo, YD., He, Q. et al. 3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising. J. Comput. Sci. Technol. 33, 838–848 (2018). https://doi.org/10.1007/s11390-018-1859-7

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  • DOI: https://doi.org/10.1007/s11390-018-1859-7

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