Intrinsic decomposition from a single RGB-D image with sparse and non-local priors

This paper proposes a new intrinsic image decomposition method that decomposes a single RGB-D image into reflectance and shading components. We observe and verify that, a shading image mainly contains smooth regions separated by curves, and its gradient distribution is sparse. We therefore use ℓ₁-norm to model the direct irradiance component - the main sub-component extracted from shading component. Moreover, a non-local prior weighted by a bilateral kernel on a larger neighborhood is designed to fully exploit structural correlation in the reflectance component to improve the decomposition performance. The model is solved by the alternating direction method under the augmented Lagrangian multiplier (ADM-ALM) framework. Experimental results on both synthetic and real datasets demonstrate that the proposed method yields better results and enjoys lower complexity compared with two state-of-the-art methods.