Snapshot fiber spectral imaging using speckle correlations and compressive sensing

TL;DR

This paper introduces a compact spectral imaging method using multicore multimode fiber and speckle correlations, enabling fast, high-resolution spectral data acquisition suitable for portable hyperspectral sensors.

Contribution

It demonstrates a novel fiber-based spectral imager that combines speckle pattern analysis with compressive sensing for efficient spectral reconstruction.

Findings

Achieved sub-nanometer spectral resolution with a multicore fiber

Successfully reconstructed spectral images using l1-minimization and clustering

Validated accurate broadband spectral reconstruction in non-compressive mode

Abstract

Snapshot spectral imaging is rapidly gaining interest for remote sensing applications. Acquiring spatial and spectral data within one image promotes fast measurement times, and reduces the need for stabilized scanning imaging systems. Many current snapshot technologies, which rely on gratings or prisms to characterize wavelength information, are difficult to reduce in size for portable hyperspectral imaging. Here, we show that a multicore multimode fiber can be used as a compact spectral imager with sub-nanometer resolution, by encoding spectral information within a monochrome CMOS camera. We characterize wavelength-dependent speckle patterns for up to 3000 fiber cores over a broad wavelength range. A clustering algorithm is employed in combination with l$_{1}$-minimization to limit data collection at the acquisition stage for the reconstruction of spectral images that are sparse in the wavelength domain. We also show that in the non-compressive regime these techniques are able to accurately reconstruct broadband information.