Intrinsic image decomposition by hierarchical L0 sparsity

This paper presents a hierarchical approach to single image intrinsic decomposition based on non-local L₀ sparsity. In contrast to previous studies using heuristic methods to well-define the ill-posed problem, our approach is able to effectively construct sparse, non-local and multiscale reflectance dependencies in an unsupervised manner, thus is less dependent on the chromaticity feature and more accurately captures the global reflectance correlations. Besides, we impose homogenous smoothness prior and scale constraint in our model to further improve the decomposition accuracy. We formulate the decomposition as a quadratic minimization problem, which can be efficiently solved in closed form. Extensive experiments show that our approach can successfully extract the shading and reflectance components from a single image, and outperforms state-of-the-art methods on benchmark dataset. Besides, our approach can achieve comparable results with user-assisted methods on natural scenes.