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We address the problem of image denoising in additive white noise without placing restrictive assumptions on its statistical distribution.
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M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Transactions on Image Processing, vol. 15, no. 12, pp. 3736–3745, Dec. 2006
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F. Luisier, T. Blu, and M. Unser, “A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding,” IEEE Transactions on Image Processing , vol. 16, no. 3, pp. 593–606, 2007
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T. Blu and F. Luisier, “The SURE-LET approach to image denoising,” IEEE Transactions on Image Processing, vol. 16, no. 11, pp. 2778 − - 2786, Nov. 2007
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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Transactions on Image Processing , vol. 16, no. 8, pp. 2080–2095, 2007
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M. Zhang and B. Günturk, “Multiresolution bilateral filtering for image denoising,” IEEE Transactions on Image Processing , vol. 17, no. 12, pp. 2324–2333, 2008
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F. Luisier, T. Blu, and P. J. Wolfe, “A CURE for noisy magnetic resonance images: Chi-square unbiased risk estimation,” IEEE Transactions on Image Processing, vol. 21, no. 8, August 2012
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P. Chatterjee and P. Milanfar, “Patch-based near-optimal image denoising,” IEEE Trans. Image Process., vol. 21, no. 4, pp. 1635–1649, Apr. 2012
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B. Panisetti, T. Blu, and C. S. Seelamantula, “An unbiased risk estimator for multiplicative noise – Application to 1-D signal denoising,” Proc. 19 t h 19^{th} Intl. Workshop on Digital Signal Processing, 2014
2014
Closest in time.
C. S. Seelamantula and T. Blu, “Image denoising in multiplicative noise,” IEEE International Conference on Image Processing, 2015, manuscript under review
2015
Closest in time.