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A Bayesian Wavelet-Based Multidimensional Deconvolution With Sub-Band Emphasis

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If you have a question about this talk, please contact Taylan Cemgil .

We proposes a new algorithm for wavelet-based multidimensional image deconvolution which employs subband-dependent minimization and the dual-tree complex wavelet transform in an iterative Bayesian framework.

In addition, this algorithm employs a new prior instead of the popular $\ell_1$ norm, and is thus able to embed a learning scheme during the iteration which helps it to achieve better deconvolution results and faster convergence.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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Average on weekdays: 9148.59046961 kWh Seminar time is 1pm BST in Room FW 11, Willam Gates Hall. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon Room GS4, Donald McIntyre Building, Faculty of Education, 184 Hills Road, Cambridge

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