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Journal of Vcibration Testing and System Dynamics 4(3) (2020) 259--268 | DOI:10.5890/JVTSD.2020.09.003

Lin-Lu Dong$^{1}$,$^{2}$, Zhi-Shuang Xue$^{1}$,$^{2}$, Xiao-Fang Liu$^{1}$,$^{3}$, Xiao-Shi Shi$^{1}$,$^{2}$

$^{1}$ School of Automation and Information Engineering, Sichuan University of Science & Engineering,

Zigong 643000, China

$^{2}$ Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and

Engineering, Zigong 643000, China

$^{3}$ School of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong

643000, China

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Abstract

In the environment of severe nuclear radiation, remote operation often uses a camera to transmit pictures to the control room. Because the noise generated by the current electronic equipment under the interference of nuclear radiation is random and irregular, the traditional denoising methods cannot meet the requirement of denoising noisy image effectively. Aiming at this problem, a denoising method based on image restoration technology is proposed. Firstly, some representative images are intercepted from the video of actual nuclear radiation field as experimental objects. Then these images are smoothed to get the region A where the pixel difference between the noisy mage and the smoothed image is larger than the threshold and the region B where the pixel difference between the smoothed image and the clean image is greater than the threshold to form two regions to be restored. Finally, the process of image restoring is performed on the two regions respectively. The two images after restoration are fused to obtain the final image. The experiment shows that our method has good performance in protecting the details of the image while removing the nuclear radiation noise. Compared with other methods, our method can obtain a more clear image.

Acknowledgments

This research was supported in part by Sichuan Science and Technology Program under Grant 2017GZ0303, in part by Fund Project of Sichuan Provincial Academician (Expert) Workstation under Grant 2016YS-GZZ01 and in part by Special Fund for Training High Level Innovative Talents of Sichuan University of Science and Engineering under Grant B12402005.

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