Compressive spectral integral imaging using a microlens array

Weiyi Feng; Hoover Rueda; Chen Fu; Chen Qian; Gonzalo R. Arce

doi:10.1117/12.2224135

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4 May 2016 Compressive spectral integral imaging using a microlens array

Weiyi Feng, Hoover Rueda, Chen Fu, Chen Qian, Gonzalo R. Arce

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Proceedings Volume 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics; 985706 (2016) https://doi.org/10.1117/12.2224135
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, Maryland, United States

Abstract

In this paper, a compressive spectral integral imaging system using a microlens array (MLA) is proposed. This system can sense the 4D spectro-volumetric information into a compressive 2D measurement image on the detector plane. In the reconstruction process, the 3D spatial information at different depths and the spectral responses of each spatial volume pixel can be obtained simultaneously. In the simulation, sensing of the 3D objects is carried out by optically recording elemental images (EIs) using a scanned pinhole camera. With the elemental images, a spectral data cube with different perspectives and depth information can be reconstructed using the TwIST algorithm in the multi-shot compressive spectral imaging framework. Then, the 3D spatial images with one dimensional spectral information at arbitrary depths are computed using the computational integral imaging method by inversely mapping the elemental images according to geometrical optics. The simulation results verify the feasibility of the proposed system. The 3D volume images and the spectral information of the volume pixels can be successfully reconstructed at the location of the 3D objects. The proposed system can capture both 3D volumetric images and spectral information in a video rate, which is valuable in biomedical imaging and chemical analysis.

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Weiyi Feng, Hoover Rueda, Chen Fu, Chen Qian, and Gonzalo R. Arce "Compressive spectral integral imaging using a microlens array", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 985706 (4 May 2016); https://doi.org/10.1117/12.2224135

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