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Neural Computing and Applications

Erschienen in:

09.10.2018 | Machine Learning Applications for Self-Organized Wireless Networks

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

verfasst von: Ling Ding, Huyin Zhang, Jinsheng Xiao, Bijun Li, Shejie Lu, Mohammad Norouzifard

Erschienen in: Neural Computing and Applications | Sonderheft 1/2019

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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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Titel: An improved image mixed noise removal algorithm based on super-resolution algorithm and CNN
verfasst von: Ling Ding
Huyin Zhang
Jinsheng Xiao
Bijun Li
Shejie Lu
Mohammad Norouzifard
Publikationsdatum: 09.10.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3777-6

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