The use of multi-aperture cameras is one of the modern trends for imaging devices, both consumer-grade and professional. This paper presents the creation of multi-aperture cameras based on long-focus single diffraction lenses. These lenses are several times better than the common lenses in terms of weight and cost, but they are significantly inferior in quality of the resulting image, and therefore they require computational reconstruction stage. We introduce various schemes of multi-aperture diffraction lenses, allowing to increase both the viewing angle and the resolution of the imaging system. We propose a convolutional neural network for image reconstruction in multi-aperture diffraction optical systems.
A hyperspectrometer based on the Offner scheme was investigated. Spectral characteristics were studied and calibrated using a standard spectrometer. As a result of estimating the deviations of the spectra of the imaging hyperspectrometer and the reference spectrometer, calibration coefficients were obtained. The reflectance spectra of beets, onions and potatoes under natural solar illumination were experimentally obtained. Based on the analysis of hyperspectral imaging data, an analysis of the distribution of vegetative indices and, in particular, moisture content, was carried out. Analysis of histograms of moisture content index distribution was carried out.
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