Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)

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Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf, Peter Gehler


We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we present competitive results by integrating an additional edge model. We believe that our approach is a solid starting point for future development in this domain.