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A. Ben Hamza and H. Krim. Image denoising: A nonlinear robust statistical approach. IEEE Trans. Signal Processing, 49(12):3045--3054, 2001.

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Unifying Probabilistic and Variational Estimation - Hamza, Krim, Unal (2002)   (Correct)

....weight functions [17] 1] PM Equation: An Estimation Theoretic Perspective In a similar spirit as above, one may proceed to justify the PM equation from a specific statistical model. Assuming an image uu ij = as a random matrix with i.i.d. elements, the output of the Log Cauchy filter [21] is defined as a solution to the maximum log likelihood estimation problem for a Cauchy distribution with dispersion c and estimation parameter #. In other words, the output of a Log Cauchy filter is the solution to the following robust estimation problem [21] min log ( min ( ## ij ....

....the output of the Log Cauchy filter [21] is defined as a solution to the maximum log likelihood estimation problem for a Cauchy distribution with dispersion c and estimation parameter #. In other words, the output of a Log Cauchy filter is the solution to the following robust estimation problem [21]: min log ( min ( ## ij ij c ij ij cu Fu = 22 where the cost function F c coincides with the Lagrangian function which yields the PM equation. Hence, in the # 6. Log Cauchy filtering: a) impulsive noise and (b) filtered image. The performance of a filter clearly ....

A. Ben Hamza and H. Krim, "Image denoising: A nonlinear robust statistical approach," IEEE Trans. Signal Processing, vol. 49, pp. 3045-3054, Dec. 2001.


Image Reconstruction by Linear Programming - Koji Tsuda And   (Correct)

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A. Ben Hamza and H. Krim. Image denoising: A nonlinear robust statistical approach. IEEE Trans. Signal Processing, 49(12):3045--3054, 2001.


Image Reconstruction by Linear Programming - Koji Tsuda And (2003)   (Correct)

No context found.

A. Ben Hamza and H. Krim. Image denoising: A nonlinear robust statistical approach. IEEE Trans. Signal Processing, 49(12):3045--3054, 2001.

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