Spectral encoded computational ghost imaging

Abstract

Multispectral imaging can acquire images of a scene in multiple spectral bands and has been widely applied in many fields, such as satellite remote sensing and geological exploration. Traditional multispectral imaging employs dispersive optical devices or narrowband filters to separate different wavelengths of light and then uses an array detector or many single-pixel detectors to retrieve them separately. These techniques suffer from large data volumes and low efficiency. In this paper, a spectral encoded strategy is proposed to realize multispectral imaging with computational ghost imaging technology. The projected illumination patterns are produced by means of deterministic orthogonal Hadamard basis patterns fused with random spectral encoded matrices, which represent different spectral information. The evolutionary compressive sensing technique is employed to restore the fused multispectral image of the color object of interest, and then a compressive sensing algorithm is applied to restore the spectral channel images. The validity of the technique is verified by a numerical simulation and an experiment. The proposed method allows a multispectral image simultaneously by only one single-pixel detector, which simplifies the structure of the spectral imaging setup and reduces the amount of data acquired and reconstruction time, and thus improving multispectral imaging efficiency.