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

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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 Signal Processing and Communications Lab Seminars series.

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