Shadow removal has evolved as a pre-processing step for various computer vision tasks. Several studies have been carried out over the past two decades to eliminate shadows from videos and images. Accurate shadow detection is an open problem because it is often considered difficult to interpret whether the darkness of a surface is contributed by a shadow incident on it or not. This paper introduces a color-model based technique to remove shadows from images. We formulate shadow removal as an optimization problem that minimizes the dissimilarities between a shadow area and its non-shadow counterpart. To achieve this, we map each shadow region to a set of non-shadow pixels, and compute an anchor value from the non-shadow pixels. The shadow region is then modified using a factor computed from the anchor value using particle swarm optimization. We demonstrate the efficiency of our technique on indoor shadows, outdoor shadows, soft shadows, and document shadows, both qualitatively and quantitatively.

中文翻译：

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通过使用非阴影锚点值进行优化来去除单个图像阴影

阴影去除已发展为各种计算机视觉任务的预处理步骤。在过去的二十年中进行了数项研究，以消除视频和图像中的阴影。精确的阴影检测是一个未解决的问题，因为通常认为很难解释表面的暗度是否是入射在其上的阴影所造成的。本文介绍了一种基于颜色模型的技术来去除图像中的阴影。我们将阴影去除公式化为优化问题，以最大程度地减少阴影区域与其非阴影对应部分之间的差异。为此，我们将每个阴影区域映射到一组非阴影像素，然后从非阴影像素计算锚定值。然后使用粒子群优化，使用根据锚值计算出的因子来修改阴影区域。