Time-data tradeoff for the sparse and cosparse regularizations of physics-driven inverse problems
Résumé
We investigate the computational performance of the sparse vs cosparse regularizations applied to physics-driven inverse problems, relative to the amount of measurements. Our results show that, despite nominal equivalence of the two models in the given context, the analysis-based optimization benefits from an increase in the volume of available data, while the synthesis one does not.
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