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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.

Single image shadow removal by optimization using non-shadow anchor values