Intrinsic images decomposition using a local and global sparse representation of reflectance

Intrinsic image decomposition is an important problem that targets the recovery of shading and reflectance components from a single image. While this is an ill-posed problem on its own, we propose a novel approach for intrinsic image decomposition using a reflectance sparsity prior that we have developed. Our method is based on a simple observation: neighboring pixels usually have the same reflectance if their chromaticities are the same or very similar. We formalize this sparsity constraint on local reflectance, and derive a sparse representation of reflectance components using data-driven edge-avoiding-wavelets. We show that the reflectance component of natural images is sparse in this representation. We also propose and formulate a novel global reflectance sparsity constraint. Using this sparsity prior and global constraints, we formulate a l₁-regularized least squares minimization problem for intrinsic image decomposition that can be solved efficiently. Our algorithm can successfully extract intrinsic images from a single image, without using other reflection or color models or any user interaction. The results on challenging scenes demonstrate the power of the proposed technique.