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Blind sparsity constrained inverse problems in volumetric imaging

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Magnetic resonance force microscopy is an emerging inverse problem in which the object domain has a natural sparseness property. See-through-wall radar imaging is another application where sparsity naturally occurs in the object domain. When the forward operator is only partially known the blind sparsity constrained problem becomes relevant. We will describe approaches to solving these naturally sparse inverse problems that rely on physics modeling, sparsity penalization, and optimization.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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