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Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 838–848 | Cite as

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

  • Bei-Ji Zou
  • Yun-Di Guo
  • Qi He
  • Ping-Bo Ouyang
  • Ke Liu
  • Zai-Liang Chen
Regular Paper
  • 90 Downloads

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.

Keywords

block matching convolutional neural network (CNN) denoising 3D filtering 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Bei-Ji Zou
    • 1
    • 2
  • Yun-Di Guo
    • 1
    • 3
  • Qi He
    • 1
    • 3
  • Ping-Bo Ouyang
    • 1
    • 3
  • Ke Liu
    • 2
  • Zai-Liang Chen
    • 1
    • 3
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Center for Information and Automation of China Nonferrous Metals Industry AssociationChangshaChina
  3. 3.Center for Ophthalmic Imaging ResearchCentral South UniversityChangshaChina

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