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
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1–4), 259–268 (1992)
Selesnick, I.: Total variation denoising (an MM algorithm). NYU Polytechnic School of Engineering Lecture Notes (2012)
Zhang, M., Gunturk, B.K.: Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. 17(12), 2324–2333 (2008)
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)
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)
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)
Romano, Y., Elad, M.: Boosting of image denoising algorithms. SIAM J. Imaging Sci. 8(2), 1187–1219 (2015)
Talebi, H., Zhu, X., Milanfar, P.: How to SAIF-ly boost denoising performance. IEEE Trans. Image Process. 22(4), 1470–1485 (2013)
Acknowledgments
This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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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