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arXiv · 2103.01908

Structural Sparsity in Multiple Measurements

Florian Boßmann·Sara Krause-Solberg·Johannes Maly·Nada Sissouno
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Abstract

We propose a novel sparsity model for distributed compressed sensing in the multiple measurement vectors (MMV) setting. Our model extends the concept of row-sparsity to allow more general types of structured sparsity arising in a variety of applications like, e.g., seismic exploration and non-destructive testing. To reconstruct structured data from observed measurements, we derive a non-convex but well-conditioned LASSO-type functional. By exploiting the convex-concave geometry of the functional, we design a projected gradient descent algorithm and show its effectiveness in extensive numerical simulations, both on toy and real data.

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Florian Boßmann, Sara Krause-Solberg, Johannes Maly, Nada Sissouno. 2021-03-02. Structural Sparsity in Multiple Measurements. https://doi.org/10.1109/tsp.2021.3137599

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