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Deep Neural Image Denoising

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Computer Vision and Graphics (ICCVG 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9972))

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Abstract

Presence of noise poses a common problem in image recognition tasks. In this paper we propose and analyse architecture of convolutional neural network capable of image denoising. We evaluate its performance with various types of artificial distortions present, with both known and unknown noise conditions. Finally, we measure how including denoising procedure in image recognition pipeline influences classification accuracy.

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References

  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Ghemawat, S., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016). arXiv preprint arXiv:1603.04467

  2. Agostinelli, F., Anderson, M.R., Lee, H.: Adaptive multi-column deep neural networks with application to robust image denoising. In: Advances in Neural Information Processing Systems, pp. 1493–1501 (2013)

    Google Scholar 

  3. Bengio, Y., Goodfellow, I.J., Courville, A.: Deep Learning. An MIT Press Book in Preparation. MIT Press, Cambridge (2015)

    Google Scholar 

  4. Bovik, A.C.: Handbook of Image and Video Processing. Academic press, Orlando (2010)

    MATH  Google Scholar 

  5. Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2392–2399. IEEE, June 2012

    Google Scholar 

  6. Cireşan, D., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)

    Article  Google Scholar 

  7. Coates, A., Ng, A.Y., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: International Conference on Artificial Intelligence and Statistics, pp. 215–223 (2011)

    Google Scholar 

  8. Cyganek, B., Siebert, J.P.: An Introduction to 3D Computer Vision Techniques and Algorithms. Wiley, Hoboken (2011)

    MATH  Google Scholar 

  9. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. In: Electronic Imaging 2006, pp. 606414–606414. International Society for Optics and Photonics, February 2006

    Google Scholar 

  10. Eigen, D., Krishnan, D., Fergus, R.: Restoring an image taken through a window covered with dirt or rain. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 633–640 (2013)

    Google Scholar 

  11. Jain, V., Seung, S.: Natural image denoising with convolutional networks. In: Advances in Neural Information Processing Systems, pp. 769–776 (2009)

    Google Scholar 

  12. Kayser, M., Zhong, V.: Denoising Convolutional Autoencoders for Noisy Speech Recognition

    Google Scholar 

  13. Koziarski, M., Cyganek, B.: Examination of the deep neural networks in classification of distorted signals. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 680–688. Springer, Heidelberg (2016). doi:10.1007/978-3-319-39384-1_60

    Chapter  Google Scholar 

  14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  15. Nair, V., Hinton, G.E. Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)

    Google Scholar 

  16. Paris, S., Durand, F.: A fast approximation of the bilateral filter using a signal processing approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 568–580. Springer, Heidelberg (2006). doi:10.1007/11744085_44

    Chapter  Google Scholar 

  17. Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2809–2813. IEEE, July 2011

    Google Scholar 

  18. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556

  19. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  20. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, 1998, pp. 839–846. IEEE, January 1998

    Google Scholar 

  21. Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)

    Google Scholar 

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Acknowledgement

This research was supported in part by PLGrid Infrastructure.

This work was supported by the Polish National Science Centre under the grant no. DEC-2014/15/B/ST6/00609.

Author information

Authors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Michał Koziarski

  2. AGH University of Science and Technology, Kraków, Poland

    Bogusław Cyganek

Authors
  1. Michał Koziarski
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  2. Bogusław Cyganek
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Corresponding author

Correspondence to Michał Koziarski .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences , Warsaw, Poland

    Leszek J. Chmielewski

  2. Computer Sci and Software Engineering, University of Western Australia , Perth, Australia

    Amitava Datta

  3. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences SGGW , Warsaw, Poland

    Ryszard Kozera

  4. Institute of Computer Science, Silesian University of Technology , Gliwice, Poland

    Konrad Wojciechowski

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Koziarski, M., Cyganek, B. (2016). Deep Neural Image Denoising. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-46418-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46417-6

  • Online ISBN: 978-3-319-46418-3

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