Technology of Graphic & Image
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3179-3182

Image distortion judgement based on Gabor wavelet and CNN

Li Pengcheng
Wu Tao
Zhang Shanqing
School of Computer Science & Technology, Hangzhou Dianzi University, Hangzhou 310018, China

Abstract

For image distortion classification, this paper proposed an algorithm based on Gabor wavelet and CNN. It used the good characteristic of Gabor wavelet to extract rough feature of images firstly, and then used the improved CNN to extract the key feature from rough feature. The main steps included pre-processing image firstly(including labels setting, samples balance and samples expansion); then it calculated eight directions Gabor wavelet to preprocessed images, and added eight sub-bands to one sample for training; finally, it used a self-designed CNN and Softmax classifier to train the final model, and used the methods of random gradient descent and error back propagation to optimize the parameters of convolution kernels during training. Using the final model to determine the type of image distortion, the classification accuracy on the LIVE standard image library is 95.62%. It shows that the proposed method has high accuracy and robustness.

Foundation Support

浙江省重点研发计划资助项目(2017C01022)
浙江省基础公益研究计划资助项目(LGG18F020013)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.03.0231
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 10
Section: Technology of Graphic & Image
Pages: 3179-3182
Serial Number: 1001-3695(2019)10-067-3179-04

Publish History

[2019-10-05] Printed Article

Cite This Article

李鹏程, 吴涛, 张善卿. 基于Gabor小波和CNN的图像失真类型判定算法 [J]. 计算机应用研究, 2019, 36 (10): 3179-3182. (Li Pengcheng, Wu Tao, Zhang Shanqing. Image distortion judgement based on Gabor wavelet and CNN [J]. Application Research of Computers, 2019, 36 (10): 3179-3182. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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