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Deep Learning for Image Denoising: A Survey

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Genetic and Evolutionary Computing (ICGEC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 834))

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Abstract

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technique has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising in recent years. Moreover, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising.

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References

  1. Ahn, B., Cho, N.I.: Block-matching convolutional neural network for image denoising (2017). arXiv:1704.00524

  2. Arora, S., Bhaskara, A., Ge, R., Ma, T.: Provable bounds for learning some deep representations. In: International Conference on Machine Learning, pp. 584–592 (2014)

    Google Scholar 

  3. Bako, S., Vogels, T., McWilliams, B., Meyer, M., Novák, J., Harvill, A., Sen, P., Derose, T., Rousselle, F.: Kernel-predicting convolutional networks for denoising monte carlo renderings. ACM Trans. Graph 36(4), 97 (2017)

    Article  Google Scholar 

  4. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)

    Article  Google Scholar 

  5. Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: CVPR 2005. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, vol. 2, pp. 60–65. IEEE (2005)

    Google Scholar 

  6. Buades, A., Coll, B., Morel, J.-M.: Nonlocal image and movie denoising. Int. J. Comput. Vis. 76(2), 123–139 (2008)

    Article  Google Scholar 

  7. Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256–1272 (2017)

    Article  Google Scholar 

  8. Choi, J.H., Elgendy, O., Chan, S.H.: Integrating disparate sources of experts for robust image denoising (2017). arXiv:1711.06712

  9. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  10. Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)

    Article  MathSciNet  Google Scholar 

  11. Esser, P., Sutter, E., Ommer, B.: A variational u-net for conditional appearance and shape generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8857–8866 (2018)

    Google Scholar 

  12. Fei, L., Lu, G., Jia, W., Teng, S., Zhang, D.: Feature extraction methods for palmprint recognition: a survey and evaluation. IEEE Trans. Syst. Man Cybern.: Syst. (2018)

    Google Scholar 

  13. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  14. Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862–2869 (2014)

    Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  16. Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., Torr, P.: Deeply supervised salient object detection with short connections. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5300–5309. IEEE (2017)

    Google Scholar 

  17. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv:1502.03167

  18. 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 

  19. Lan, X., Roth, S., Huttenlocher, D., Black, M.J.: Efficient belief propagation with learned higher-order Markov random fields. In: European Conference on Computer Vision, pp. 269–282. Springer (2006)

    Google Scholar 

  20. Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)

    Article  Google Scholar 

  21. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  22. Lefkimmiatis, S.: Universal denoising networks: a novel CNN architecture for image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3204–3213 (2018)

    Google Scholar 

  23. Li, S., Li, W., Cook, C., Zhu, C., Gao, Y.: Independently recurrent neural network (indrnn): Building a longer and deeper RNN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5457–5466 (2018)

    Google Scholar 

  24. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., Van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  25. Qin, Y., Tian, C.: Weighted feature space representation with kernel for image classification. Arab. J. Sci. Eng. 1–13 (2017)

    Google Scholar 

  26. Schmidt, U., Roth, S.: Shrinkage fields for effective image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2774–2781 (2014)

    Google Scholar 

  27. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks (2013). arXiv:1312.6229

  28. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

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

  30. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  31. Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: a persistent memory network for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4539–4547 (2017)

    Google Scholar 

  32. Tian, C., Zhang, Q., Sun, G., Song, Z., Li, S.: Fft consolidated sparse and collaborative representation for image classification. Arab. J. Sci. Eng. 43(2), 741–758 (2018)

    Article  Google Scholar 

  33. Tripathi, S., Lipton, Z.C., Nguyen, T.Q.: Correction by projection: denoising images with generative adversarial networks (2018). arXiv:1803.04477

  34. Wang, H., Kläser, A., Schmid, C., Liu, C.-L.: Action recognition by dense trajectories. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3169–3176. IEEE (2011)

    Google Scholar 

  35. Wang, L., Liu, T., Wang, G., Chan, K.L., Yang, Q.: Video tracking using learned hierarchical features. IEEE Trans. Image Process. 24(4), 1424–1435 (2015)

    Article  MathSciNet  Google Scholar 

  36. Wang, T., Sun, M., Hu, K.: Dilated residual network for image denoising (2017). arXiv:1708.05473

  37. Wang, G., Wang, G., Pan, Z., Zhang, Z.: Multiplicative noise removal using deep CNN denoiser prior. In: 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 1–6. IEEE (2017)

    Google Scholar 

  38. Wen, J., Fang, X., Yong, X., Tian, C., Fei, L.: Low-rank representation with adaptive graph regularization. Neural Netw. 108, 83–96 (2018)

    Article  Google Scholar 

  39. Wu, Y., He, K.: Group normalization (2018). arXiv:1803.08494

    Chapter  Google Scholar 

  40. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987–5995. IEEE (2017)

    Google Scholar 

  41. Xie, W., Li, Y., Jia, X.: Deep convolutional networks with residual learning for accurate spectral-spatial denoising. Neurocomputing (2018)

    Google Scholar 

  42. Xu, J., Zhang, L., Zuo, W., Zhang, D., Feng, X.: Patch group based nonlocal self-similarity prior learning for image denoising. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 244–252 (2015)

    Google Scholar 

  43. You, Y., Zhang, Z., Hsieh, C.-J., Demmel, J., Keutzer, K.: 100-epoch imagenet training with alexnet in 24 minutes (2017)

    Google Scholar 

  44. Zagoruyko, S., Komodakis, N.: Diracnets: training very deep neural networks without skip-connections (2017). arXiv:1706.00388

  45. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  46. Zhang, L., Zuo, W.: Image restoration: from sparse and low-rank priors to deep priors. IEEE Signal Process. Mag. 34(5), 172–179 (2017)

    Article  Google Scholar 

  47. Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN based image denoising. IEEE Trans. Image Process. (2018)

    Google Scholar 

  48. Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 6 (2018)

    Google Scholar 

  49. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  50. Zhu, Z., Wu, W., Zou, W., Yan, J.: End-to-end flow correlation tracking with spatial-temporal attention. Illumination 42, 20 (2017)

    Google Scholar 

Download references

Acknowledgements

This paper was supported in part by Shenzhen Municipal Science and Technology Innovation Council under Grant no. JCYJ20170811155725434, in part by the National Natural Science Foundation under Grant no. 61876051.

Author information

Authors and Affiliations

  1. Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, Shenzhen, Guangdong, 518055, China

    Chunwei Tian, Yong Xu & Ke Yan

  2. Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory, Harbin Institute of Technology, Shenzhen, Shenzhen, Guangdong, 518055, China

    Chunwei Tian, Yong Xu & Ke Yan

  3. School of Computers, Guangdong University of Technology, Guangzhou, 510006, China

    Lunke Fei

Authors
  1. Chunwei Tian
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  2. Yong Xu
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  3. Lunke Fei
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  4. Ke Yan
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Corresponding author

Correspondence to Yong Xu .

Editor information

Editors and Affiliations

  1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences, Bergen, China

    Jerry Chun-Wei Lin

  3. Changzhou College of Information Technology, Changzhou, Jiangsu, China

    Bixia Sui

  4. College of Smart Living and Management, Tajen University, Pingtung, Taiwan

    Shih-Pang Tseng

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Tian, C., Xu, Y., Fei, L., Yan, K. (2019). Deep Learning for Image Denoising: A Survey. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_59

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