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.
翻译:我们处理将单一图像分解成反光和阴影的问题。 难度来自以下事实:图像- 表面反光、 直接光照、 环境光照- 大量结合在观测到的图像中。 我们提议用相对亮度订购像素来推断阴影, 而不事先了解图像组件的绝对值。 双向阴影订单以两种方式估算: 亮度顺序和本地阴影字段的低序装配。 亮度顺序是一种非本地的测量, 它可以适用于任何一对像素的像素, 包括反映和阴影的像素。 低序装配用于当地平滑的阴影区域中的像素配对。 两者加起来, 它们可以同时捕捉全球秩序结构的绝对值和本地阴影部分的变异。 我们提议一个CSIS- 识别模型(CSF), 将对齐的顺序整合成一个全球一致的顺序。 迭代选择过程可以解决任何对等的像值的像值, 包括反映和阴影颜色的颜色, 其反映的像系的颜色和阴影值的颜色, 将自动地标定为不可靠, 。