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

@Article{Ding2018,
author="Ding, Ling
and Zhang, Huyin
and Xiao, Jinsheng
and Li, Bijun
and Lu, Shejie
and Norouzifard, Mohammad",
title="An improved image mixed noise removal algorithm based on super-resolution algorithm and CNN",
journal="Neural Computing and Applications",
year="2018",
month="Oct",
day="09",
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.",
issn="1433-3058",
doi="10.1007/s00521-018-3777-6",
url="https://doi.org/10.1007/s00521-018-3777-6"
}