Superresolution without Separation
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jun 2015.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 3.
6 papers.
- 2022Off-the-grid learning of mixtures from a continuous dictionarycited by 2
- 2025Super-Resolution with Structured Motioncited by 1
- 2026A Dual Certificate Approach to Sparsity in Infinite-Width Shallow Neural Networkscited by 1
- 2021The Nonconvex Geometry of Linear Inverse Problemscited by 0
- 2026Characterization of the Basin of Convexity for Multi-Snapshot Spike Deconvolution via Variable Projectioncited by 0
- 2026On the Optimisation Landscape of Overparameterised Weighted MMDcited by 0
- 2026On the Optimisation Landscape of Overparameterised Weighted MMD14 Oct 2026
- 2026Characterization of the Basin of Convexity for Multi-Snapshot Spike Deconvolution via Variable Projection10 Jul 2026
- 2026A Dual Certificate Approach to Sparsity in Infinite-Width Shallow Neural Networks18 Mar 2026
- 2025Super-Resolution with Structured Motion21 May 2025
- 2022Off-the-grid learning of mixtures from a continuous dictionary29 Jun 2022
- 2021The Nonconvex Geometry of Linear Inverse Problems7 Jan 2021