  We present a method to separate a single image captured under two
illuminants, with different spectra, into the two images corresponding to the
appearance of the scene under each individual illuminant. We do this by
training a deep neural network to predict the per-pixel reflectance
chromaticity of the scene, which we use in conjunction with a previous
flash/no-flash image-based separation algorithm to produce the final two output
images. We design our reflectance chromaticity network and loss functions by
incorporating intuitions from the physics of image formation. We show that this
leads to significantly better performance than other single image techniques
and even approaches the quality of the two image separation method.
