Comparing supervised and unsupervised learning in image denoising

Research Article
Open access

Comparing supervised and unsupervised learning in image denoising

Hanyun Wang 1*
  • 1 Chongqing Foreign Language School, Chongqing, 400065, China    
  • *corresponding author whywhy_Whyyyzbb@163.com
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

Recent studies on unsupervised learning have attracted people's increasing attention. In particular, Deep learning has developed rapidly in recent years. With the development of media images, people's demand for image noise reduction is increasing, and the requirements are becoming more and more strict. The traditional methods used for image noise reduction are far from meeting people's requirements, and people are eager to find a more efficient image noise reduction technology. In recent years, the technology of using a convolutional neural network for image noise reduction has become more and more skilled. This paper explores the reliability of image noise reduction technology using a convolutional neural network as an autoencoder, and whether good performance is maintained without using clean images. The article aims to compare the performance with supervised learning and unsupervised learning by deep learning in image denoising.

Keywords:

Wang,H. (2023). Comparing supervised and unsupervised learning in image denoising. Applied and Computational Engineering,5,284-291.
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References

[1]. Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M.» and Aila, T. Noise2noise: Learning image restoration without clean data. Technical report, 2018.

[2]. Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y.» Liao, R, Zhou, J., and Wang, G. Low-dose ct with a residual encoder-decoder convolutional neural network. Technical report, 2017.

[3]. Shan, H., Zhang, Y. Yang, Q., Kruger, U., Kalra, M. K., Sun,

[4]. Yang, Q・,Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra,

[5]. Gholizadeh-Ansari, M.，Alirezaie, J., and Babyn, R Deep learning for low-dose ct denoising using perceptual loss and edge detection layer. Technical report, 2020.

[6]. Choi, K., Kim, S. W., and Lim, J. S. Real-time image reconstruction for low-dose ct using deep convolutional generative adversarial networks (gans). Technical report, 2018.

[7]. L.,Cong, W., and Wang, G. 3-d convolutional encoder¬decoder network for low-dose ct via transfer learning from a 2-d trained network. Technical report, 2018.

[8]. M. K., Zhang, Y., Sun, L.» and Wang, G. Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. Technical report, 2018.

Cite this article

Wang,H. (2023). Comparing supervised and unsupervised learning in image denoising. Applied and Computational Engineering,5,284-291.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M.» and Aila, T. Noise2noise: Learning image restoration without clean data. Technical report, 2018.

[2]. Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y.» Liao, R, Zhou, J., and Wang, G. Low-dose ct with a residual encoder-decoder convolutional neural network. Technical report, 2017.

[3]. Shan, H., Zhang, Y. Yang, Q., Kruger, U., Kalra, M. K., Sun,

[4]. Yang, Q・,Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra,

[5]. Gholizadeh-Ansari, M.，Alirezaie, J., and Babyn, R Deep learning for low-dose ct denoising using perceptual loss and edge detection layer. Technical report, 2020.

[6]. Choi, K., Kim, S. W., and Lim, J. S. Real-time image reconstruction for low-dose ct using deep convolutional generative adversarial networks (gans). Technical report, 2018.

[7]. L.,Cong, W., and Wang, G. 3-d convolutional encoder¬decoder network for low-dose ct via transfer learning from a 2-d trained network. Technical report, 2018.

[8]. M. K., Zhang, Y., Sun, L.» and Wang, G. Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. Technical report, 2018.

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