# Unlocking Off-the-Grid Sparse Recovery with Unlimited Sensing: Simultaneous Super-Resolution in Time and Amplitude

arXiv: https://arxiv.org/abs/2510.16948 · HTML: https://mlsanity.com/paper/2510.16948/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 19 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Exact Support Recovery for Sparse Spikes Deconvolution](https://mlsanity.com/paper/1306.6909/) (2013)
- [On Unlimited Sampling and Reconstruction](https://mlsanity.com/paper/1905.03901/) (2019)
- [Unlimited Sampling from Theory to Practice: Fourier-Prony Recovery and Prototype ADC](https://mlsanity.com/paper/2105.05818/) (2021)
- [The Sliding Frank-Wolfe Algorithm and its Application to Super-Resolution Microscopy](https://mlsanity.com/paper/1811.06416/) (2018)
- [Geometry and Symmetry in Short-and-Sparse Deconvolution](https://mlsanity.com/paper/1901.00256/) (2019)
- [Back in the US-SR: Unlimited Sampling and Sparse Super-Resolution with its Hardware Validation](https://mlsanity.com/paper/2207.13230/) (2022)
- [Modulo Sampling of FRI Signals](https://mlsanity.com/paper/2207.08774/) (2022)
- [λ-MIMO: Massive MIMO via Modulo Sampling](https://mlsanity.com/paper/2210.10193/) (2022)
- [Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution](https://mlsanity.com/paper/1904.04283/) (2019)
- [The Modulo Radon Transform: Theory, Algorithms and Applications](https://mlsanity.com/paper/2105.04194/) (2021)
- [Fourier-Domain Inversion for the Modulo Radon Transform](https://mlsanity.com/paper/2307.13114/) (2023)
- [Full-Duplex Beyond Self-Interference: The Unlimited Sensing Way](https://mlsanity.com/paper/2404.08610/) (2024)
- [Unleashing Dynamic Range and Resolution in Unlimited Sensing Framework via Novel Hardware](https://mlsanity.com/paper/2410.20193/) (2024)
- [Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels](https://mlsanity.com/paper/2411.00893/) (2024)
- [USF Spectral Estimation: Prevalence of Gaussian Cram'er-Rao Bounds Despite Modulo Folding](https://mlsanity.com/paper/2505.03098/) (2025)

## Abstract (arXiv)

The recovery of Dirac impulses, or spikes, from filtered measurements is a classical problem in signal processing. As the spikes lie in the continuous domain while measurements are discrete, this task is known as super-resolution or off-the-grid sparse recovery. Despite significant theoretical and algorithmic advances over the past decade, these developments often overlook critical challenges at the analog-digital interface. In particular, when spikes exhibit strong-weak amplitude disparity, conventional digital acquisition may result in clipping of strong components or loss of weak ones beneath the quantization noise floor. This motivates a broader perspective: super-resolution must simultaneously resolve both amplitude and temporal structure. Under a fixed bit budget, such information loss is unavoidable. In contrast, the emerging theory and practice of the Unlimited Sensing Framework (USF) demonstrate that these fundamental limitations can be overcome. Building on this foundation, we demonstrate that modulo encoding within USF enables digital super-resolution by enhancing measurement precision, thereby unlocking temporal super-resolution beyond conventional limits. We develop new theoretical results that extend to non-bandlimited kernels commonly encountered in practice and introduce a robust algorithm for off-the-grid sparse recovery. To demonstrate practical impact, we instantiate our framework in the context of time-of-flight imaging. Both numerical simulations and hardware experiments validate the effectiveness of our approach under low-bit quantization, enabling super-resolution in amplitude and time.