Superresolution without Separation

June 10, 2015 Β· Declared Dead Β· πŸ› IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

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Authors Geoffrey Schiebinger, Elina Robeva, Benjamin Recht arXiv ID 1506.03144 Category math.OC: Optimization & Control Cross-listed cs.IT Citations 81 Venue IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Last Checked 5 months ago
Abstract
This paper provides a theoretical analysis of diffraction-limited superresolution, demonstrating that arbitrarily close point sources can be resolved in ideal situations. Precisely, we assume that the incoming signal is a linear combination of M shifted copies of a known waveform with unknown shifts and amplitudes, and one only observes a finite collection of evaluations of this signal. We characterize properties of the base waveform such that the exact translations and amplitudes can be recovered from 2M + 1 observations. This recovery is achieved by solving a a weighted version of basis pursuit over a continuous dictionary. Our methods combine classical polynomial interpolation techniques with contemporary tools from compressed sensing.
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