Snapshot hyperspectral imaging via spectral basis multiplexing in Fourier domain

TL;DR

This paper introduces a low-cost snapshot hyperspectral imaging method using spectral basis multiplexing in the Fourier domain, enabling dynamic scene observation with simplified encoding and decoding.

Contribution

It proposes a novel Fourier domain multiplexing approach that reduces spectral and spatial data for efficient hyperspectral imaging in dynamic scenes.

Findings

High-quality reconstruction demonstrated on simulation data

Prototype system validated experimentally

Efficient spectral and spatial data compression achieved

Abstract

Hyperspectral imaging is an important tool having been applied in various fields, but still limited in observation of dynamic scenes. In this paper, we propose a snapshot hyperspectral imaging technique which exploits both spectral and spatial sparsity of natural scenes. Under the computational imaging scheme, we conduct spectral dimension reduction and spatial frequency truncation to the hyperspectral data cube and snapshot it in a low cost manner. Specifically, we modulate the spectral variations by several broadband spectral filters, and then map these modulated images into different regions in the Fourier domain. The encoded image compressed in both spectral and spatial are finally collected by a monochrome detector. Correspondingly, the reconstruction is essentially a Fourier domain extraction and spectral dimensional back projection with low computational load. This Fourier-spectral multiplexing in a 2D sensor simplifies both the encoding and decoding process, and makes hyperspectral data captured in a low cost manner. We demonstrate the high performance of our method by quantitative evaluation on simulation data and build a prototype system experimentally for further validation.