International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Image Enhanced Machine Vision System for Smart Factory

  • Kim, ByungJoo (Department of Electrical and Electronic Engineering, Youngsan University)
  • Received : 2021.01.13
  • Accepted : 2021.01.27
  • Published : 2021.05.31
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Abstract

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

Keywords

Acknowledgement

This work was supported by Youngsan University Research Fund of (2020).

References

  1. T. Brooks, B. Mildenhall, T. Xue, J. Chen, D. Sharlet, and J. T. Barron, "Unprocessing images for learned raw denoising," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11036-11045, 2019. DOI: 10.1109/CVPR.2019.01129
  2. U. Dmitry, V. Andrea, and L. Victor, "Deep image prior," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 9446-9454, 2018. DOI: 10.1109/CVPR.2018.00984
  3. K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, "Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising," IEEE transactions on image processing, Vol. 26, No. 7, pp. 3142-3155, 2017. DOI: 10.1109/TIP.2017.2662206
  4. J. Kim, "Edge-Preserving and Adaptive Transmission Estimation for Effective Single Image Haze Removal," International Journal of Internet, Broadcasting and Communication, Vol. 12, No 2, pp. 21-29, 2020. DOI : https://doi.org/10.7236/IJIBC.2020.12.2.21
  5. C. Doersch, "Tutorial on variational autoencoders," arXiv preprint arXiv:1606.05908, 2016.
  6. https://www.compthree.com/blog/autoencoder/
  7. https://www.pyimagesearch.com/2020/02/24/denoising-autoencoders-with-keras-tensorflow-and-deep-learning/
  8. http://yann.lecun.com/exdb/mnist/
  9. T. N. Sainath, A. R. Mohamed, B. Kingsbury and B. Ramabhadran, "Deep convolutional neural networks for LVCSR," In 2013 IEEE international conference on acoustics, speech and signal processing , pp. 8614-8618, May 2013. DOI: 10.1109/ICASSP.2013.6639347
  10. A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, Vol. 25, pp. 1097-1105, 2012. https://doi.org/10.1145/3065386
  11. T. N. Sainath, B. Kingsbury, A. R. Mohamed, G. E. Dahl, G. Saon, H. Soltau and B. Ramabhadran, "Improvements to deep convolutional neural networks for LVCSR," In 2013 IEEE workshop on automatic speech recognition and understanding, pp. 315-320, Dec. 2013. DOI: 10.1109/ASRU.2013.6707749
  12. A. Khan, A. Sohail, U. Zahoora and A. S. Qureshi, "A survey of the recent architectures of deep convolutional neural networks," Artificial Intelligence Review, Vol. 53, No. 8, pp. 5455-5516, 2020. Doi : 10.1007/s10462-020-09825-6
  13. H. Xiao, K. Rasul, and R. Vollgraf, "Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms," arXiv preprint arXiv:1708.07747, 2017.
  14. D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.