Decomposing an image into the shading and reflectance layers remains challenging due to its severely under-constrained nature. We present an approach based on illumination decomposition that recovers the intrinsic images without additional information, e.g., depth or user interaction. Our approach is based on the rationale that the shading component contains the step and drift channels simultaneously. We decompose the illumination into two channels: the step shading, corresponding to the sharp shading changes due to cast shadow or abrupt shape changes; the drift shading, accounting for the smooth shading variations due to gradual illumination changes or slow shape changes. Due to such transformation of turning the conventional assumption that shading has smoothness as reasonable prior, our model has the advantages in handling real images, especially with the cast shadows or strong shape edges. We also apply a much stricter edge classifier along with a reinforcement process to enhance our method. We formulate the problem using a two-parameter energy function and split it into two energy functions corresponding to the reflectance and step shading. Experiments on the MIT dataset, the IIW dataset and the MPI Sintel dataset have shown the success of our approach over the state-of-the-art methods.

中文翻译：

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内在的图像分解，具有阶跃和漂移阴影分离

由于图像的严重约束不足，将图像分解为阴影和反射层仍然具有挑战性。我们提出了一种基于照明分解的方法，该方法无需其他信息即可恢复原始图像，例如深度或用户交互。我们的方法基于基本原理，即阴影部分同时包含阶跃和漂移通道。我们将照明分解为两个通道：阶跃阴影，对应于由于投射阴影或形状突然变化而引起的急剧阴影变化；漂移阴影，说明由于逐渐的光照变化或缓慢的形状变化而导致的平滑阴影变化。由于这种转换方式转变成传统的假设，即阴影具有的平滑度应为合理的先验值，因此我们的模型具有处理真实图像的优势，特别是在投射阴影或形状强烈的边缘时。我们还将应用更严格的边缘分类器以及增强过程来增强我们的方法。我们使用两参数能量函数来表述问题，并将其分为与反射率和阶跃阴影相对应的两个能量函数。在MIT数据集，IIW数据集和MPI Sintel数据集上的实验表明，我们的方法优于最新方法。