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A Note on Boosting Algorithms for Image Denoising

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Image Analysis and Recognition (ICIAR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10317))

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Abstract

In recent years, non-local methods have been among most efficient tools to address the classical problem of image denoising. Recently, Romano et al. have proposed a novel algorithm aimed at “boosting” of a number of non-local denoising algorithms as a “black-box.” In this manuscript, we consider this algorithm and derive an analytical expression corresponding to successive applications of their proposed “boosting scheme.” Mathematically, we prove that such successive application does not always enhance the input image and is equivalent to a re-parameterization of the original “boosting” algorithm. We perform a set of computational experiments on test images to support this claim. Finally, we conclude that considering the blind application of such boosting methods as a general remedy for all denoising schemes is questionable.

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References

  1. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1–4), 259–268 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  2. Selesnick, I.: Total variation denoising (an MM algorithm). NYU Polytechnic School of Engineering Lecture Notes (2012)

    Google Scholar 

  3. Zhang, M., Gunturk, B.K.: Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. 17(12), 2324–2333 (2008)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  5. 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 

  6. Charest, M.R., Elad, M., Milanfar, P.: A general iterative regularization framework for image denoising. In: 2006 40th Annual Conference on Information Sciences and Systems, pp. 452–457. IEEE (2006)

    Google Scholar 

  7. Romano, Y., Elad, M.: Boosting of image denoising algorithms. SIAM J. Imaging Sci. 8(2), 1187–1219 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Talebi, H., Zhu, X., Milanfar, P.: How to SAIF-ly boost denoising performance. IEEE Trans. Image Process. 22(4), 1470–1485 (2013)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Correspondence to Mehran Ebrahimi .

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© 2017 Springer International Publishing AG

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Falconer, C., Bohun, C.S., Ebrahimi, M. (2017). A Note on Boosting Algorithms for Image Denoising. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-59876-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59875-8

  • Online ISBN: 978-3-319-59876-5

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