T. Frese, C. A. Bouman, and K. Sauer, "Adaptive wavelet graph model for Bayesian tomographic reconstruction," IEEE Transactions on Image Processing, vol. 11, pp. 756--770, July 2002.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....tractable, the density is discretized to a xed number of values (on the order of twenty) A prior is also introduced to favour local smoothness, according to some statistical description of regularity. This technique was recently extended to multiresolution for increased eciency and robustness [46]. Wavelets have also been used to extend FBP for CT reconstruction [97, 12, 92, 127] The basic idea is to apply the wavelet transform at the level of the 1D projections, which in turn induces a multiscale decomposition of the 2D object. For a xed level of detail, this method is equivalent to ....

....segmentation technique gives good results for complicated geometries and an extremely sparse number of views. As few as three to ve views are needed for a good segmentation, compared to the dozens of views required by statistical reconstruction methods designed for handling noise and limited data [99, 12, 46]. One problem with the method is that the heuristic nature of the energy function could entail laborious handtuning of the parameters or modi cation of the prior for each new problem instance. Furthermore, the simultaneous evolution of more than one or two di erent level sets is dicult to ....

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T. Frese, C. A. Bouman, and K. Sauer. Adaptive wavelet graph model for Bayesian tomographic reconstruction. IEEE Transactions on Image Processing, 11(7):756{ 770, July 2002.

....multiresolution annealing algorithms and demonstrate that there are potentially some advantages to this approach that result from the usual multigrid coarse to fine philosophy of using coarser grids to guide solutions at finer ones. We also refer the reader to other multiresolution models [289, 212, 125, 74, 127, 213] that involve structures other than trees, together with algorithmic structures that are reminiscent of multigrid and coarse to fine procedures. One final point to make about the methods mentioned in the preceding paragraph concerns the modeling of the measurements. One of the critical properties ....

T. Frese, C.A. Bouman, and K. Sauer. Adaptive wavelet graph model for Bayesian tomographic reconstruction. IEEE Trans. on Image Processing, in review 2001.

....[11] 12] 13] 14] 15] have been shown to be effective in many imaging problems. Wavelets have been studied for Bayesian tomography [16] 17] 18] 19] 20] and both wavelet and multiresolution models have been applied in Bayesian formulations of emission tomography [21] 22] 23] [24] and thermal wave tomography [25] For ODT, a two resolution wavelet decomposition was used to speed inversion of a problem linearized with a Born approximation [26] Multigrid methods are a special class of multiresolution algorithms which work by recursively operating on the data at different ....

T. Frese, C. A. Bouman, and K. Sauer, "Adaptive wavelet graph model for Bayesian tomographic reconstruction," IEEE Trans. on Image Processing, vol. 11, no. 7, pp. 756--770, July 2002.

No context found.

T. Frese, C. A. Bouman, and K. Sauer, "Adaptive wavelet graph model for Bayesian tomographic reconstruction," IEEE Transactions on Image Processing, vol. 11, pp. 756--770, July 2002.

No context found.

T. Frese, C. A. Bouman, and K. Sauer, "Adaptive wavelet graph model for Bayesian tomographic reconstruction," IEEE Transactions on Image Processing, vol. 11, pp. 756--770, July 2002.

No context found.

T. Frese, C. A. Bouman, and K. Sauer. Adaptive wavelet graph model for Bayesian tomographic reconstruction. IEEE Transactions on Image Processing, 11(7):756-- 770, July 2002.

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