Skip to main content
Book cover

Science and Information Conference

CVC 2019: Advances in Computer Vision pp 416–433Cite as

No-reference Image Denoising Quality Assessment

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 943)

Abstract

A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a no-reference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and denoising models alone cannot robustly rank denoising results, they often complement each other. We accordingly design denoising quality features based on these existing metrics and models and then use Random Forests Regression to aggregate them into a more powerful unified metric. Our experiments on images with various types and levels of noise show that our no-reference denoising quality assessment method significantly outperforms the state-of-the-art quality metrics. This paper also provides a method that leverages our quality assessment method to automatically tune the parameter settings of a denoising algorithm for an input noisy image to produce an optimal denoising result.

Keywords

  • Image denoising
  • Quality assessment
  • Random forests regression

This is a preview of subscription content, access via your institution.

Chapter
EUR   29.95
Price includes VAT (Finland)
  • DOI: 10.1007/978-3-030-17795-9_31
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
EUR   181.89
Price includes VAT (Finland)
  • ISBN: 978-3-030-17795-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
EUR   241.99
Price includes VAT (Finland)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.
Fig. 14.
Fig. 15.

References

  1. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)

    CrossRef  Google Scholar 

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

    Google Scholar 

  3. Chen, F., Zhang, L., Yu, H.: External patch prior guided internal clustering for image denoising. In: IEEE International Conference on Computer Vision (ICCV), pp. 603–611 (2015)

    Google Scholar 

  4. Chen, J., Tang, C., Wang, J.: Noise brush: interactive high quality image-noise separation. ACM Trans. Graph. 28(5), 146:1–146:10 (2009)

    Google Scholar 

  5. Chen, X., Kang, S.B., Yang, J., Yu, J.: Fast patch-based denoising using approximated patch geodesic paths. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1211–1218 (2013)

    Google Scholar 

  6. Chen, Z., Jiang, T., Tian, Y.: Quality assessment for comparing image enhancement algorithms. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3003–3010 (2014)

    Google Scholar 

  7. 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)

    CrossRef  MathSciNet  Google Scholar 

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

    CrossRef  MathSciNet  Google Scholar 

  9. Gu, K., Wang, S., Yang, H., Lin, W., Zhai, G., Yang, X., Zhang, W.: Saliency-guided quality assessment of screen content images. IEEE Trans. Multimed. PP(99), 1 (2016)

    Google Scholar 

  10. Gu, K., Wang, S., Zhai, G., Ma, S., Yang, X., Lin, W., Zhang, W., Gao, W.: Blind quality assessment of tone-mapped images via analysis of information, naturalness, and structure. IEEE Trans. Multimed. 18(3), 432–443 (2016)

    CrossRef  Google Scholar 

  11. Gu, K., Zhai, G., Yang, X., Zhang, W.: Using free energy principle for blind image quality assessment. IEEE Trans. Multimed. 17(1), 50–63 (2015)

    CrossRef  Google Scholar 

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

    Google Scholar 

  13. Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 419–426 (2006)

    Google Scholar 

  14. Kendall, M.: A new measure of rank correlation. Biometrika 30, 81–93 (1938)

    CrossRef  Google Scholar 

  15. Knaus, C., Zwicker, M.: Dual-domain image denoising. In: 2013 IEEE International Conference on Image Processing (ICIP), pp. 440–444 (2013)

    Google Scholar 

  16. Kong, X., Li, K., Yang, Q., Wenyin, L., Yang, M.H.: A new image quality metric for image auto-denoising. In: IEEE International Conference on Computer Vision (ICCV), pp. 2888–2895 (2013)

    Google Scholar 

  17. Levin, A., Nadler, B., Durand, F., Freeman, W.T.: Patch complexity, finite pixel correlations and optimal denoising. In: European Conference on Computer Vision (ECCV), pp. 73–86 (2012)

    CrossRef  Google Scholar 

  18. Li, S., Zhang, F., Ma, L., Ngan, K.N.: Image quality assessment by separately evaluating detail losses and additive impairments. IEEE Trans. Multimed. 13(5), 935–949 (2011)

    CrossRef  Google Scholar 

  19. Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)

    Google Scholar 

  20. Liu, Y., Wang, J., Cho, S., Finkelstein, A., Rusinkiewicz, S.: A no-reference metric for evaluating the quality of motion deblurring. ACM Trans. Graph. 32(6), 175:1–175:12 (2013)

    Google Scholar 

  21. Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z.: Rating image aesthetics using deep learning. IEEE Trans. Multimed. 17(11), 2021–2034 (2015)

    CrossRef  Google Scholar 

  22. Luo, Y., Tang, X.: Photo and video quality evaluation: focusing on the subject. In: European Conference on Computer Vision (ECCV), pp. 386–399. Springer (2008)

    Google Scholar 

  23. Mittal, A., Moorthy, A., Bovik, A.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    CrossRef  MathSciNet  Google Scholar 

  24. Mittal, A., Soundararajan, R., Bovik, A.: Making a “completely blind” image quality analyzer. IEEE Sig. Process. Lett. 20(3), 209–212 (2013)

    CrossRef  Google Scholar 

  25. Moorthy, A., Bovik, A.: A two-step framework for constructing blind image quality indices. IEEE Sig. Process. Lett. 17(5), 513–516 (2010)

    CrossRef  Google Scholar 

  26. Mosseri, I., Zontak, M., Irani, M.: Combining the power of internal and external denoising. In: IEEE International Conference on Computational Photography (ICCP), pp. 1–9 (2013)

    Google Scholar 

  27. Roth, S., Black, M.: Fields of experts: a framework for learning image priors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 860–867 (2005)

    Google Scholar 

  28. Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1), 259–268 (1992)

    CrossRef  MathSciNet  Google Scholar 

  29. Saad, M., Bovik, A., Charrier, C.: A DCT statistics-based blind image quality index. IEEE Sig. Process. Lett. 17(6), 583–586 (2010)

    CrossRef  Google Scholar 

  30. Tang, H., Joshi, N., Kapoor, A.: Learning a blind measure of perceptual image quality. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 305–312 (2011)

    Google Scholar 

  31. Tang, X., Luo, W., Wang, X.: Content-based photo quality assessment. IEEE Trans. Multimed. 15(8), 1930–1943 (2013)

    CrossRef  Google Scholar 

  32. Tian, X., Dong, Z., Yang, K., Mei, T.: Query-dependent aesthetic model with deep learning for photo quality assessment. IEEE Trans. Multimed. 17(11), 2035–2048 (2015)

    CrossRef  Google Scholar 

  33. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision (ICCV), pp. 839–846 (1998)

    Google Scholar 

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

    CrossRef  Google Scholar 

  35. Weiss, B.: Fast median and bilateral filtering. ACM Trans. Graph. 25(3), 519–526 (2006)

    CrossRef  Google Scholar 

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

    Google Scholar 

  37. Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1098–1105 (2012)

    Google Scholar 

  38. Yu, G., Sapiro, G.: DCT image denoising: a simple and effective image denoising algorithm. Image Process. Line 1, 292–296 (2011)

    Google Scholar 

  39. Yue, H., Sun, X., Yang, J., Wu, F.: CID: combined image denoising in spatial and frequency domains using web images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2933–2940 (2014)

    Google Scholar 

  40. Zhang, L., Vaddadi, S., Jin, H., Nayar, S.: Multiple view image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1542–1549 (2009)

    Google Scholar 

  41. Zhu, X., Milanfar, P.: Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Process. 19(12), 3116–3132 (2010)

    CrossRef  MathSciNet  Google Scholar 

  42. Zontak, M., Irani, M.: Internal statistics of a single natural image. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 977–984 (2011)

    Google Scholar 

  43. Zontak, M., Mosseri, I., Irani, M.: Separating signal from noise using patch recurrence across scales. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1195–1202 (2013)

    Google Scholar 

  44. Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: IEEE International Conference on Computer Vision (ICCV), pp. 479–486 (2011)

    Google Scholar 

  45. Zuo, W., Zhang, L., Song, C., Zhang, D.: Texture enhanced image denoising via gradient histogram preservation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1203–1210 (2013)

    Google Scholar 

Download references

Acknowledgements

This work was supported by NSF grants IIS-1321119.

Author information

Authors and Affiliations

  1. Portland State University, Portland, USA

    Si Lu

Authors
  1. Si Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Si Lu .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Prof. Dr. Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Lu, S. (2020). No-reference Image Denoising Quality Assessment. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-17795-9_31

Download citation