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

Snapshot hyperspectral imaging via spectral basis multiplexing in Fourier domain