A fast denoising fusion network using internal and external priors

Abstract

As a preprocessing module, denoising can affect the overall image processing; thus, image denoising algorithms are of high significance for image processing and have been studied for several decades. Theoretically, the performances of existing algorithms can be significantly improved, but these improvements are indeed slowing down. To significantly improve the denoising performance, we propose a denoising network method called the fast denoising fusion network (FDFNet). It combines the advantages of a neural network based on block matching and 3D filtering (BM3D-Net) and a fast and flexible denoising convolutional neural network (FFDNet), which simultaneously utilizes internal and external priors to remove noise in a given image; thus, it is a fast and efficient denoising method that delivers superior performance. BM3D-Net and FFDNet can generate two images as basic estimates for fusion. We adopt a combination model to receive the two estimates, which can fuse them effectively to obtain a latent image. Through testing on standard datasets, our experimental results reveal that FDFNet outperformed state-of-the-art denoising methods in terms of both subjective and objective quality. By implementing the entire method on a CNN, the proposed method could exploit the GPU to achieve a higher efficiency. Because the proposed method combines internal and external priors effectively, it could utilize complementary prior knowledge to derive more information.

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References

  1. 1.

    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’05), vol. 2, pp. 60–65. IEEE, New York (2005)

  2. 2.

    Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)

    MathSciNet  Article  Google Scholar 

  3. 3.

    Zhang, Q., Li, B.: Discriminative K-SVD for dictionary learning in face recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2691–2698. IEEE, New York (2010)

  4. 4.

    Yang, M., Zhang, L., Feng, X., et al.: Fisher discrimination dictionary learning for sparse representation. In: 2011 International Conference on Computer Vision, pp. 543–550. IEEE, New York (2011)

  5. 5.

    Dong, W., Zhang, L., Shi, G., et al.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2012)

    MathSciNet  Article  Google Scholar 

  6. 6.

    Xu, J., Zhang, L., Zhang, D.: A trilateral weighted sparse coding scheme for real-world image denoising. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 20–36 (2018)

  7. 7.

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

    MathSciNet  Article  Google Scholar 

  8. 8.

    Gu, S., Zhang, L., Zuo, W., et al.: 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)

  9. 9.

    Jain, V., Seung, S.: Natural image denoising with convolutional networks. Adv. Neural Inf. Process. Syst. 2009, 769–776 (2009)

    Google Scholar 

  10. 10.

    Mao, X., Shen, C., Yang, Y.-B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Adv. Neural Inf. Process. Syst. 2016, 2802–2810 (2016)

  11. 11.

    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. IEEE, New York (2012)

  12. 12.

    Santhanam, V., Morariu, V.I., Davis, L.S.: Generalized deep image to image regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5609–5619 (2017)

  13. 13.

    Zhang, K., Zuo, W., Gu, S., et al.: Learning deep CNN denoiser prior for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3929–3938 (2017)

  14. 14.

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

    MathSciNet  Article  Google Scholar 

  15. 15.

    Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018)

    MathSciNet  Article  Google Scholar 

  16. 16.

    Choi, J.H., Elgendy, O.A., Chan, S.H.: Optimal combination of image denoisers. IEEE Trans. Image Process. (2019). https://doi.org/10.1109/TIP.2019.2903321

    MathSciNet  Article  MATH  Google Scholar 

  17. 17.

    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012)

    Google Scholar 

  18. 18.

    Yue, H., Sun, X., Yang, J., et al.: Image denoising by exploring external and internal correlations. IEEE Trans. Image Process. 24(6), 1967–1982 (2015)

    MathSciNet  Article  Google Scholar 

  19. 19.

    Yang, D., Sun, J.: Bm3d-net: a convolutional neural network for transform-domain collaborative filtering. IEEE Signal Process. Lett. 25(1), 55–59 (2017)

    Article  Google Scholar 

  20. 20.

    Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  21. 21.

    Zhang, L., Zhang, L., Mou, X., et al.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    MathSciNet  Article  Google Scholar 

  22. 22.

    Xu, J., Zhang, L., Zuo, W., et al.: 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)

  23. 23.

    Alsaiari, A., Rustagi, R., Thomas, M.M., et al.: Image denoising using a generative adversarial network. In: 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT), pp. 126–132. IEEE, New York (2019)

  24. 24.

    Martin, D., Fowlkes, C., Tal, D., et al.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. ICCV Vancouver (2001)

  25. 25.

    Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1692–1700 (2018)

  26. 26.

    Xu, J., Li, H., Liang, Z., et al.: Real-world noisy image denoising: a new benchmark. Preprint arXiv:1804.02603 (2018)

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61662044, 51765042, and 61163023, in part by the Jiangxi Provincial Natural Science Foundation under Grant 20171BAB202017, and in part by the Jiangxi Provincial Graduate Innovation Special Fund under Grant YC2018-S066.

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Correspondence to Shaoping Xu.

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Luo, J., Xu, S. & Li, C. A fast denoising fusion network using internal and external priors. SIViP (2021). https://doi.org/10.1007/s11760-021-01858-w

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Keywords

  • Image denoising
  • Convolutional neural networks
  • Image priors
  • Image fusion