Signal–reference correlation for modal structure retrieval in disordered optical fields

We experimentally demonstrate a signal–reference modal correlation method for retrieving structured optical modal signatures after propagation through a scattering medium. Hermite–Gaussian and Laguerre–Gaussian modes transmitted through the medium produce randomized speckle patterns in direct intensity measurements, obscuring their spatial structure. By computing the spatial cross-correlation between each signal speckle and a single fixed Gaussian reference, distinct modal morphology emerges in the central correlation peak. Cartesian parity symmetry is recovered for Hermite–Gaussian modes, while charge-dependent radial scaling is resolved for Laguerre–Gaussian modes. The approach requires no transmission-matrix calibration, wavefront shaping, or machine-learning-assisted reconstruction, providing a compact and experimentally accessible framework for structured-light discrimination in complex scattering environments.

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Publication Details

Journal
Optics Letters
Published
2026-04-27
DOI
https://doi.org/10.1364/ol.596836
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

Signal–reference correlation for modal structure retrieval in disordered optical fields

Yukti Pandey, Raj Kumar, Neha Joshi, Nandan Singh
Optics Letters
Neural Networks and Reservoir Computing
article

Signal–reference correlation for modal structure retrieval in disordered optical fields

Yukti Pandey, Raj Kumar, Neha Joshi, Nandan Singh
article en

Abstract

We experimentally demonstrate a signal–reference modal correlation method for retrieving structured optical modal signatures after propagation through a scattering medium. Hermite–Gaussian and Laguerre–Gaussian modes transmitted through the medium produce randomized speckle patterns in direct intensity measurements, obscuring their spatial structure. By computing the spatial cross-correlation between each signal speckle and a single fixed Gaussian reference, distinct modal morphology emerges in the central correlation peak. Cartesian parity symmetry is recovered for Hermite–Gaussian modes, while charge-dependent radial scaling is resolved for Laguerre–Gaussian modes. The approach requires no transmission-matrix calibration, wavefront shaping, or machine-learning-assisted reconstruction, providing a compact and experimentally accessible framework for structured-light discrimination in complex scattering environments.

Optics LettersVol. 51(10)
SingHealth (SG), Central Scientific Instruments Organisation (IN)
Openalex Percentile: Top 15%
Neural Networks and Reservoir Computing
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