Spectral classification of sparse photon depth images

By illumination of target scenes using a set of different wavelengths, we demonstrate color classification of scenes, as well as depth estimation, in photon-starved images. The spectral signatures are classified with a new advanced statistical image processing method from measurements of the same scene, in this case using combinations of 33, 16, 8 or 4 different wavelengths in the range 500 – 820 nm. This approach makes it possible to perform color classification and depth estimation on images containing as few as one photon per pixel, on average. Compared to single wavelength imaging, this approach improves target discrimination by extracting more spectral information, which, in turn, improves the depth estimation since this approach is robust to changes in target reflectivity. We demonstrate color classification and depth profiling of complex targets at average signal levels as low as 1.0 photons per pixel from as few as 4 different wavelength measurements.