Compressive Imaging of Subwavelength Structures

TL;DR

This paper explores subwavelength imaging using compressed sensing, analyzing stability and resolution limits, and demonstrating that resolution improves with higher SNR, supported by numerical simulations.

Contribution

It introduces a compressed sensing framework for subwavelength imaging, including a novel sampling scheme and analysis of stability and resolution limits.

Findings

Subwavelength modes are generally unstable without additional techniques.

Resolution improves inversely with SNR in high SNR regimes.

Numerical simulations confirm theoretical predictions.

Abstract

The problem of imaging extended targets (sources or scatterers) is formulated in the framework of compressed sensing with emphasis on subwavelength resolution. The proposed formulation of the problems of inverse source/scattering is essentially exact and leads to the random partial Fourier measurement matrix. In the case of square-integrable targets, the proposed sampling scheme in the Littlewood-Paley wavelet basis block-diagonalizes the scattering matrix with each block in the form of random partial Fourier matrix corresponding to each dyadic scale of the target. The resolution issue is analyzed from two perspectives: stability and the signal-to-noise ratio (SNR). The subwavelength modes are shown to be typically unstable. The stability in the subwavelength modes requires additional techniques such as near-field measurement or illumination. The number of the stable modes typically increases as the negative $d$-th (the dimension of the target) power of the distance between the target and the sensors/source. The resolution limit is shown to be inversely proportional to the SNR in the high SNR limit. Numerical simulations are provided to validate the theoretical predictions.