Extraction of spectral information using liquid crystal (LC) retarders has recently become a topic of great interest because of its importance for creating hyper- and multispectral images in a compact and inexpensive way. However, this method of hyperspectral imaging requires thick LC-layer retarders (50 µm–100 µm and above) to obtain spectral modulation signals for reliable signal reconstruction. This makes the device extremely slow in the case of nematic LCs (NLCs), since the response time of NLCs increases proportionally to the square of the LC-layer thickness, which excludes fast dynamic processes monitoring. In this paper, we explore two approaches for solving the speed problem: the first is based on the use of faster nanospiral ferroelectric liquid crystals as an alternative to NLCs, and the second is based on using a passive multiband filter and focuses on multispectral extraction rather than hyperspectral. A detailed comparative study of nematic and ferroelectric devices is presented. The study is carried out using a 9-spectral bands passive spectral filter, covering the visible and near-infrared ranges. We propose the concept of multispectral rather than hyperspectral extraction, where a small number of wavelengths are sufficient for specific applications.
Computational 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.
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