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Image Processing with Multiscale Stochastic Models (1993)  (Make Corrections)  (18 citations)
Mark R. Luettgen



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Abstract: In this thesis, we develop image processing algorithms and applications for a particular class of multiscale stochastic models. First, we provide background on the model class, including a discussion of its relationship to wavelet transforms and the details of a two-sweep algorithm for estimation. A multiscale model for the error process associated with this algorithm is derived. Next, we illustrate how the multiscale models can be used in the context of regularizing ill-posed inverse problems... (Update)

Context of citations to this paper:   More

.... shown to be useful in defining alternative approaches to problems in computer vision which are often posed in a variational context [9, 10, 12, 13]. These previous investigations, however, dealt with problems that resulted in linear estimation algorithms. In contrast, image...

.... the MAR framework can effectively model a wide range of Gaussian stochastic processes, including one dimensional Markov processes [31, 33], 1 f like processes [8, 11, 12, 32, 60] and Markov random fields [32, 33] An additional benefit of the MAR framework is that it leads...

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8:   Efficient multiscale regularization with applications to the computation of opti.. - Luettgen, Karl et al. - 1994
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6:   Modeling and estimation of multiresolution stochastic processes (context) - Basseville, Benveniste et al. - 1992

BibTeX entry:   (Update)

M. Luettgen. Image Processing with Multiscale Stochastic Models. PhD thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May 1993. http://citeseer.ist.psu.edu/luettgen93image.html   More

@phdthesis{ luettgen93image,
    author = "Mark R. Luettgen",
    title = "Image processing with multiscale stochastic models",
    pages = "217",
    year = "May 1993",
    url = "citeseer.ist.psu.edu/luettgen93image.html" }
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