Over the last decades, different imaging solutions have been proposed to achieve three-dimensional (3D) space reconstruction and obstacle detection. Either based on stereo-vision principles using active pixel (RGB) sensors operating in the visible (VIS) part of the spectra, or applying the Time-of-flight (TOF) principle based on active Near Infra-Red (NIR) illumination, to mention just a few. Suppose a silicon-based detector yields extremely low quantum efficiencies for NIR active illumination. Solutions are considered together with the huge photon noise levels produced by the background illumination accompanied by Rayleigh scattering effects in outdoor applications and the operating limitations of these systems in harsh weather conditions, especially if relatively low-power active illumination is used result evident. Suppose longer wavelengths for active illumination are applied to overcome these issues. In that case, Indium Gallium Arsenide (InGaAs) based photodetectors become the technology of choice, and for low-cost solutions, using a single InGaAs photodetector or an InGaAs line-sensor become a promising choice. In this case, the principles of Single-Pixel Imaging (SPI) and Compressive Sensing (CS) acquire paramount importance. The introduction of a Near Infra-Red Single Pixel Imaging (NIR-SPI) sensor aimed at detecting static and dynamic objects under outdoor conditions increased applications field widely from Unmanned Aerial Vehicle (UAV) to sensor redundant for vehicles.
SPI simulation results of different algorithms under different sampling ratios of : a) Sampling ratio=0.2, b) Sampling ratio=0.8, and c) Sampling ratio=3. We can see CS and TV methods need few samples for SPI reconstruction, while DGI, GD, and Poisson need a higher sampling ratio >1.
