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

  • Machine Learning Applications for Self-Organized Wireless Networks
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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.

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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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Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan, 430072, China

    Ling Ding & Huyin Zhang

  2. College of Computer Science and Technology, Hubei University of Science and Technology, Xianning, 437100, China

    Ling Ding & Shejie Lu

  3. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Jinsheng Xiao

  4. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Bijun Li

  5. School of Engineering, Computer and Mathematical Sciences, EEE Department, Auckland University of Technology, Auckland, 1142, New Zealand

    Mohammad Norouzifard

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  1. Ling Ding
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  2. Huyin Zhang
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  3. Jinsheng Xiao
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  4. Bijun Li
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  5. Shejie Lu
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  6. Mohammad Norouzifard
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Correspondence to Huyin Zhang.

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Ding, L., Zhang, H., Xiao, J. et al. An improved image mixed noise removal algorithm based on super-resolution algorithm and CNN. Neural Comput & Applic 31 (Suppl 1), 325–336 (2019). https://doi.org/10.1007/s00521-018-3777-6

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  • DOI: https://doi.org/10.1007/s00521-018-3777-6

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