Hyperspectral image recovery based on fusion of coded aperture snapshot spectral imaging and RGB images by guided filtering

Abstract In compressive hyperspectral imaging systems, the key issue is to accurately and efficiently recover 3D spectral data cubes from spectrally undersampled 2D projections. In this paper, a guided-filtering-based reconstruction algorithm is proposed to recover hyperspectral data cube with a joint coded aperture snapshot spectral imaging (CASSI) and RGB imaging system. In order to preserve the spectral and spatial details in the CASSI and the RGB branch to the greatest extent, 2D projections from the two branches are reconstructed into hyperspectral images individually with a pre-trained dictionary. The appropriate combination of the information from the two branches is realized through guided image filtering, which is applied on the reconstruction result of the CASSI branch with the result from the RGB branch as the guidance image. In virtue of combination between the well-performed spectral fidelity of the CASSI branch and the high spatial accuracy of the RGB branch, the proposed guided-filtering-based compressive hyperspectral imaging (GFCHI) not only overcomes the block effect and blurring common in the state-of-the-art methods, but also improves spectral fidelity related to key applications of hyperspectral imaging. The quantitative metrics associated with spatial, spectral and overall performance demonstrate the state-of-the-art reconstruction quality of GFCHI.