Affiliation:
1. Department of Electro‐optics and Photonics Engineering and the Ilse Katz Institute of Nanoscale Science and Technology ECE‐School Ben Gurion University of the Negev Beer Sheva 84105 Israel
2. Department of Electrical Engineering and Physics and The Center for Advanced Research in Computational Optics Sami Shamoon College of Engineering Beer Sheva 8410802 Israel
Abstract
AbstractComputational spectral imaging using reconstruction methods such as compressed sensing and deep learning is becoming popular. Despite the great progress, for multispectral imaging, only few expectations are realized due to various constraints. Here, a new method is proposed for multispectral sensing based on use of the following: (i) dual spectral modules, one defining the working spectral bands while the other as spectral modulator, and (ii) distributed 3D neural network algorithm. The method shows fast and accurate sensing, avoids a complicated calibration process, and can directly access any wavelength at any point. Experimental demonstration is presented using thin liquid crystal cells showing high peak signal‐to‐noise ratio.
Funder
Ministry of Science and Technology, Israel
Subject
Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Cited by
4 articles.
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