1 research outputs found
Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition
We address the problem of decomposing a single image into reflectance and
shading. The difficulty comes from the fact that the components of image---the
surface albedo, the direct illumination, and the ambient illumination---are
coupled heavily in observed image. We propose to infer the shading by ordering
pixels by their relative brightness, without knowing the absolute values of the
image components beforehand. The pairwise shading orders are estimated in two
ways: brightness order and low-order fittings of local shading field. The
brightness order is a non-local measure, which can be applied to any pair of
pixels including those whose reflectance and shading are both different. The
low-order fittings are used for pixel pairs within local regions of smooth
shading. Together, they can capture both global order structure and local
variations of the shading. We propose a Consistency-aware Selective Fusion
(CSF) to integrate the pairwise orders into a globally consistent order. The
iterative selection process solves the conflicts between the pairwise orders
obtained by different estimation methods. Inconsistent or unreliable pairwise
orders will be automatically excluded from the fusion to avoid polluting the
global order. Experiments on the MIT Intrinsic Image dataset show that the
proposed model is effective at recovering the shading including deep shadows.
Our model also works well on natural images from the IIW dataset, the UIUC
Shadow dataset and the NYU-Depth dataset, where the colors of direct lights and
ambient lights are quite different