RGB silicon-based image sensors operating in the visible (VIS) part of the spectra (with wavelengths between 400 and 700 nm) are highly susceptible to outdoor environmental conditions. They are forced to deal with scattering effects caused by the interaction of light with micrometer size particles in the rain, fog, or smoke, limiting the depth of visibility. Using longer wavelengths, e.g., near infra-red (NIR) radiation with wavelengths of around 1550~nm, would result in less light attenuation effects, eliminating most of Rayleigh scattering and leaving predominantly elastic Mie scattering effects, where the size of the scattering particles is comparable to the used illumination wavelengths. Motivated by the latter, this work proposes a vision system based on NIR active illumination and single-pixel imaging (SPI) using an array of 8 x 8 NIR-LEDs and a single Indium Gallium Arsenide (InGaAs) detector device with high detection efficiency for the wavelength range of interest. The proposed system uses a fusion of the super-resolution convolutional neural networks (CS-SRCNN) and the wavelet method, adapted to different Hadamard active illumination scan patterns for 2D image reconstruction.With the objective of evaluating the NIR-SPI 2D image reconstruction capabilities in scattering environments with rain, we carried out an evaluation of the quality of the reconstructed images using figures of merit such as Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) in scenarios of laboratory that simulate different conditions of background illumination and droplet size to test its feasibility as a vision sensor.
SPIE Future Sensing Technologies 2021, Nov. 14, 2021.
https://doi.org/10.1117/12.2601118.
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