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Dual Adversarial Network: Toward Real-World Noise Removal and Noise Generation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12355)

Abstract

Real-world image noise removal is a long-standing yet very challenging task in computer vision. The success of deep neural network in denoising stimulates the research of noise generation, aiming at synthesizing more clean-noisy image pairs to facilitate the training of deep denoisers. In this work, we propose a novel unified framework to simultaneously deal with the noise removal and noise generation tasks. Instead of only inferring the posteriori distribution of the latent clean image conditioned on the observed noisy image in traditional MAP framework, our proposed method learns the joint distribution of the clean-noisy image pairs. Specifically, we approximate the joint distribution with two different factorized forms, which can be formulated as a denoiser mapping the noisy image to the clean one and a generator mapping the clean image to the noisy one. The learned joint distribution implicitly contains all the information between the noisy and clean images, avoiding the necessity of manually designing the image priors and noise assumptions as traditional. Besides, the performance of our denoiser can be further improved by augmenting the original training dataset with the learned generator. Moreover, we propose two metrics to assess the quality of the generated noisy image, for which, to the best of our knowledge, such metrics are firstly proposed along this research line. Extensive experiments have been conducted to demonstrate the superiority of our method over the state-of-the-arts both in the real noise removal and generation tasks. The training and testing code is available at https://github.com/zsyOAOA/DANet.

Keywords

Real-world Denoising Generation Metric 

Supplementary material

504449_1_En_3_MOESM1_ESM.pdf (1.3 mb)
Supplementary material 1 (pdf 1367 KB)

References

  1. 1.
    Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  2. 2.
    Agostinelli, F., Anderson, M.R., Lee, H.: Adaptive multi-column deep neural networks with application to robust image denoising. Adv. Neural Inf. Process. Syst. 26, 1493–1501 (2013)Google Scholar
  3. 3.
    Anaya, J., Barbu, A.: Renoir - a benchmark dataset for real noise reduction evaluation. arXiv preprint arXiv:1409.8230 (2014). https://academic.microsoft.com/paper/1514812871
  4. 4.
    Anwar, S., Barnes, N.: Real image denoising with feature attention. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3155–3164 (2019)Google Scholar
  5. 5.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)
  6. 6.
    Barbu, A.: Training an active random field for real-time image denoising. IEEE Trans. Image Process. 18(11), 2451–2462 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Brooks, T., Mildenhall, B., Xue, T., Chen, J., Sharlet, D., Barron, J.T.: Unprocessing images for learned raw denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11036–11045 (2019)Google Scholar
  8. 8.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 60–65. IEEE (2005)Google Scholar
  9. 9.
    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, pp. 2392–2399 (2012)Google Scholar
  10. 10.
    Cao, X., et al.: Low-rank matrix factorization under general mixture noise distributions. In: The IEEE International Conference on Computer Vision (ICCV), December 2015Google Scholar
  11. 11.
    Chen, J., Chen, J., Chao, H., Yang, M.: Image blind denoising with generative adversarial network based noise modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3155–3164 (2018)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Chongxuan, L., Xu, T., Zhu, J., Zhang, B.: Triple generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 4088–4098 (2017)Google Scholar
  14. 14.
    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)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Dong, W., Shi, G., Li, X.: Nonlocal image restoration with bilateral variance estimation: a low-rank approach. IEEE Trans. Image Process. 22(2), 700–711 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  17. 17.
    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
  18. 18.
    Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein gans. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)Google Scholar
  19. 19.
    Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1712–1722 (2019)Google Scholar
  20. 20.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  21. 21.
    Huiskes, M.J., Thomee, B., Lew, M.S.: New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative. In: Proceedings of the International Conference on Multimedia Information Retrieval, pp. 527–536 (2010)Google Scholar
  22. 22.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)Google Scholar
  23. 23.
    Jain, V., Seung, S.: Natural image denoising with convolutional networks. Adv. Neural Inf. Process. Syst. 21, 769–776 (2008)Google Scholar
  24. 24.
    Jaroensri, R., Biscarrat, C., Aittala, M., Durand, F.: Generating training data for denoising real rgb images via camera pipeline simulation. arXiv preprint arXiv:1904.08825 (2019)
  25. 25.
    Kim, D.W., Ryun Chung, J., Jung, S.W.: GRDN: grouped residual dense network for real image denoising and gan-based real-world noise modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0 (2019)Google Scholar
  26. 26.
    Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: ICLR 2015: International Conference on Learning Representations 2015 (2015). https://academic.microsoft.com/paper/2964121744
  27. 27.
    Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: ICLR 2014: International Conference on Learning Representations (ICLR) 2014 (2014)Google Scholar
  28. 28.
    Lebrun, M., Buades, A., Morel, J.M.: A nonlocal Bayesian image denoising algorithm. SIAM J. Imaging Sci. 6(3), 1665–1688 (2013)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Liu, D., Wen, B., Fan, Y., Loy, C.C., Huang, T.: Non-local recurrent network for image restoration. In: NIPS 2018: The 32nd Annual Conference on Neural Information Processing Systems, pp. 1673–1682 (2018)Google Scholar
  30. 30.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2272–2279 (2009)Google Scholar
  31. 31.
    Mairal, J., Elad, M., Sapiro, G.: Sparse representation for color image restoration. IEEE Trans. Image Process. 17(1), 53–69 (2007)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Mao, X.J., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: NIPS 2016 Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 2810–2818 (2016)Google Scholar
  33. 33.
    Meng, D., De La Torre, F.: Robust matrix factorization with unknown noise. In: The IEEE International Conference on Computer Vision (ICCV), December 2013Google Scholar
  34. 34.
    Nam, S., Hwang, Y., Matsushita, Y., Joo Kim, S.: A holistic approach to cross-channel image noise modeling and its application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1683–1691 (2016)Google Scholar
  35. 35.
    Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024–8035 (2019)Google Scholar
  36. 36.
    Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1586–1595 (2017)Google Scholar
  37. 37.
    Pıtz, T., Roth, S.: Neural nearest neighbors networks. In: NIPS 2018: The 32nd Annual Conference on Neural Information Processing Systems, pp. 1087–1098 (2018)Google Scholar
  38. 38.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR 2016: International Conference on Learning Representations 2016 (2016). https://academic.microsoft.com/paper/2963684088
  39. 39.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015). https://academic.microsoft.com/paper/1901129140
  40. 40.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom. 60(1–4), 259–268 (1992)MathSciNetCrossRefGoogle Scholar
  41. 41.
    Samuel, K.G.G., Tappen, M.F.: Learning optimized map estimates in continuously-valued MRF models. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 477–484 (2009)Google Scholar
  42. 42.
    Schmidt, U.: Half-quadratic inference and learning for natural images (2017)Google Scholar
  43. 43.
    Schmidt, U., Roth, S.: Shrinkage fields for effective image restoration. In: CVPR 2014 Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2774–2781 (2014)Google Scholar
  44. 44.
    Sun, J., Tappen, M.F.: Learning non-local range Markov random field for image restoration. In: CVPR 2011, pp. 2745–2752 (2011)Google Scholar
  45. 45.
    Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4549–4557 (2017)Google Scholar
  46. 46.
    Tsin, Y., Ramesh, V., Kanade, T.: Statistical calibration of CCD imaging process. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 1, pp. 480–487. IEEE (2001)Google Scholar
  47. 47.
    Wang, R., Chen, B., Meng, D., Wang, L.: Weakly supervised lesion detection from fundus images. IEEE Trans. Med. Imaging 38(6), 1501–1512 (2018)CrossRefGoogle Scholar
  48. 48.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  49. 49.
    Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. Adv. Neural Inf. Process. Syst. 25, 341–349 (2012)Google Scholar
  50. 50.
    Xie, Q., Zhao, Q., Meng, D., Xu, Z.: Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery. IEEE Trans. Pattern Anal. Mach. Intell. 40(8), 1888–1902 (2017)CrossRefGoogle Scholar
  51. 51.
    Xu, J., Li, H., Liang, Z., Zhang, D., Zhang, L.: Real-world noisy image denoising: a new benchmark. arXiv preprint arXiv:1804.02603 (2018)
  52. 52.
    Xu, J., Zhang, L., Zhang, D.: A trilateral weighted sparse coding scheme for real-world image denoising. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 21–38. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01237-3_2CrossRefGoogle Scholar
  53. 53.
    Xu, J., Zhang, L., Zhang, D., Feng, X.: Multi-channel weighted nuclear norm minimization for real color image denoising. In: ICCV (2017)Google Scholar
  54. 54.
    Yong, H., Meng, D., Zuo, W., Zhang, L.: Robust online matrix factorization for dynamic background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 40(7), 1726–1740 (2017)CrossRefGoogle Scholar
  55. 55.
    Yue, Z., Yong, H., Meng, D., Zhao, Q., Leung, Y., Zhang, L.: Robust multiview subspace learning with nonindependently and nonidentically distributed complex noise. IEEE Trans. Neural Netw. Learn. Syst. 31(4), 1070–1083 (2019)Google Scholar
  56. 56.
    Yue, Z., Yong, H., Zhao, Q., Meng, D., Zhang, L.: Variational denoising network: toward blind noise modeling and removal. In: Advances in Neural Information Processing Systems, pp. 1688–1699 (2019)Google Scholar
  57. 57.
    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)MathSciNetCrossRefGoogle Scholar
  58. 58.
    Zhou, M., Chen, H., Ren, L., Sapiro, G., Carin, L., Paisley, J.W.: Non-parametric Bayesian dictionary learning for sparse image representations. In: Advances in Neural Information Processing Systems, pp. 2295–2303 (2009)Google Scholar
  59. 59.
    Zhu, F., Chen, G., Hao, J., Heng, P.A.: Blind image denoising via dependent Dirichlet process tree. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1518–1531 (2016)CrossRefGoogle Scholar
  60. 60.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Xi’an Jiaotong UniversityShaanxiChina
  2. 2.Hong Kong Polytechnic UniversityHong KongChina
  3. 3.DAMO Academy, Alibaba GroupShenzhenChina
  4. 4.The Macau University of Science and TechnologyMacauChina

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