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Neural Computing and Applications

, Volume 31, Supplement 1, pp 325–336 | Cite as

An improved image mixed noise removal algorithm based on super-resolution algorithm and CNN

  • Ling Ding
  • Huyin ZhangEmail author
  • Jinsheng Xiao
  • Bijun Li
  • Shejie Lu
  • Mohammad Norouzifard
Machine Learning Applications for Self-Organized Wireless Networks
  • 183 Downloads

Abstract

Based on the hardware and sensors of image acquisition, the noise in the image has been easily generated. In this paper, an improved method of image decompression has proposed the shortcoming of the above-mentioned hardware algorithm. The traditional filter desiccation algorithm can only remove one or two specific noises, and it is not effective for other types. We combine some excellent neural network models. In this paper, an image mixing noise removal algorithm based on convolution nerve has been mentioned. Aiming at realizing the super-resolution of the image, the deconvolution layer can be used only to enlarge the image. The magnification factor is the step of deconvolution. This paper aims to eliminate the interference of the image noise. The effect of magnification on the deconvolution layer is impossible. The results of experimental test show that the algorithm achieves a good noise removal effect and is suitable for various mixed noise images. The algorithm used in this paper improves the subjective visual effect and objective evaluation index.

Keywords

Hardware Filter desiccation algorithm Deconvolution layer 

Notes

Acknowledgements

This paper is funded by the National Natural Science Foundation of China (Project Nos. 41671441, 61540059, and 91120002); the Plan Project of Guangdong Provincial Science and Technology (Project No. 2015B010131007); Hubei Provincial Department of Education Guiding Project (B2016187); and Joint Fund Project (NSFC—Guangdong Big Data Science Center Project), Project No. U1611262.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Ling Ding
    • 1
    • 2
  • Huyin Zhang
    • 1
    Email author
  • Jinsheng Xiao
    • 3
  • Bijun Li
    • 4
  • Shejie Lu
    • 2
  • Mohammad Norouzifard
    • 5
  1. 1.School of ComputerWuhan UniversityWuhanChina
  2. 2.College of Computer Science and TechnologyHubei University of Science and TechnologyXianningChina
  3. 3.School of Electronic InformationWuhan UniversityWuhanChina
  4. 4.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  5. 5.School of Engineering, Computer and Mathematical Sciences, EEE DepartmentAuckland University of TechnologyAucklandNew Zealand

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