J. C. Ye, C. A. Bouman, K. J. Webb, and R. P. Millane, "Nonlinear multigrid algorithms for Bayesian optical diffusion tomography," IEEE Trans. Image Process. 10, 909 --922 #2001#.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....120] have been developed to allow e#cient zooming in on features of interest. There is also an extensive literature on the use of MRF models together with either full multigrid computational algorithms or purely coarse to fine algorithmic structures. Examples of the former can be found in [319, 70, 109, 356], where the treatment in [319] represents what to the author s knowledge is the first thorough examination of the application of multigrid methods to image processing computer vision problems. Full multigrid methods, such as those used in [319] and discussed in much more depth in references ....

....comment, we note that multiresolution methods for inverse problems have significant overlaps with the topics of both of the preceding sections. For example, the use of multigrid or coarse to fine algorithms to combat problems of computational complexity and local minima is well documented (with [63, 215, 289, 290, 126, 125, 356] representing examples) As for the use of wavelets, the motivations include not only the fact that wavelets decorrelate many stochastic processes but also that in many cases they lead to significant sparsification of the nonlocal operator relating the measurements and the underlying random field ....

J.C. Ye, C.A. Bouman, K.J. Webb, and R.P. Millane. Nonlinear multigrid algorithms for Bayesian optical di#usion tomography. IEEE Trans. on Image Processing, 10(6):909--922, June 2001.

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J. C. Ye, C. A. Bouman, K. J. Webb, and R. P. Millane, "Nonlinear multigrid algorithms for Bayesian optical diffusion tomography," IEEE Trans. Image Process. 10, 909 --922 #2001#.

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J. C. Ye, C. A. Bouman, K. J. Webb, and R. P. Millane, "Nonlinear multigrid algorithms for Bayesian optical di#usion tomography," IEEE Trans. on Image Process., vol. 10, no. 6, pp. 909--922, June 2001.

....approach to EIT based on a direct nonlinear formulation analogous to the full approximation scheme (FAS) in nonlinear multigrid PDE solvers. Johnson et al. 43] applied an algebraic multigrid algorithm to inverse bioelectric field problems formulated with the finite element method. In [44] [45], Ye, et al. formulated the multigrid approach directly in an optimization framework, and used the method to solve ODT problems. In related work, Nash and Lewis formulated multigrid algorithms for the solution of a broad class of optimization problems [46] 47] Importantly, both the approaches ....

....by using coarser grids to solve the forward model PDE. In previous approaches, the forward model PDE was solved only at the finest grid. This means that coarse grid updates were either computationally costly, or a linearization approximation was made for the coarse grid forward model [41] 44] [45]. The coarse grid forward model can be modeled by a correctly discretized PDE, preserving the nonlinear characteristics of the forward model. A wide variety of optimization methods can be used for solving the inverse problem at each grid. Hence, common methods such as pre conditioned ....

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J. C. Ye, C. A. Bouman, K. J. Webb, and R. P. Millane, "Nonlinear multigrid algorithms for Bayesian optical diffusion tomography, " IEEE Trans. on Image Processing, vol. 10, no. 6, pp. 909--922, June 2001.

No context found.

J. C. Ye, C. A. Bouman, K. J. Webb, and R. P. Millane, "Nonlinear multigrid algorithms for Bayesian optical diffusion tomography," IEEE Trans. Image Process. 10, 909--922 (2001).

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