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Time-data tradeoff for the sparse and cosparse regularizations of physics-driven inverse problems

Abstract

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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Dates and versions

hal-01147438 , version 1 (30-04-2015)
hal-01147438 , version 2 (25-06-2015)

Identifiers

  • HAL Id : hal-01147438 , version 2

Cite

Srđan Kitić, Nancy Bertin, Rémi Gribonval. Time-data tradeoff for the sparse and cosparse regularizations of physics-driven inverse problems. SPARS15 - Signal Processing with Adaptive Sparse Structured Representations, Jul 2015, Cambridge, United Kingdom. ⟨hal-01147438v2⟩
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