  Cameras that can measure the depth of each pixel in addition to its color
have become easily available and are used in many consumer products worldwide.
Often the depth channel is captured at lower quality compared to the RGB
channels and different algorithms have been proposed to improve the quality of
the D channel given the RGB channels. Typically these approaches work by
assuming that edges in RGB are correlated with edges in D.
  In this paper we approach this problem from the standpoint of natural image
statistics. We obtain examples of high quality RGBD images from a computer
graphics generated movie (MPI-Sintel) and we use these examples to compare
different probabilistic generative models of RGBD image patches. We then use
the generative models together with a degradation model and obtain a Bayes
Least Squares (BLS) estimator of the D channel given the RGB channels. Our
results show that learned generative models outperform the state-of-the-art in
improving the quality of depth channels given the color channels in natural
images even when training is performed on artificially generated images.
