| A. Ben Hamza and H. Krim. Image denoising: A nonlinear robust statistical approach. IEEE Trans. Signal Processing, 49(12):3045--3054, 2001. |
....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.
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.
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.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC