Projects per year
A user-centric method for fast, interactive, robust, and high-quality shadow removal is presented. Our algorithm can perform detection and removal in a range of difficult cases, such as highly textured and colored shadows. To perform detection, an on-the-fly learning approach is adopted guided by two rough user inputs for the pixels of the shadow and the lit area. After detection, shadow removal is performed by registering the penumbra to a normalized frame, which allows us efficient estimation of nonuniform shadow illumination changes, resulting in accurate and robust removal. Another major contribution of this work is the first validated and multiscene category ground truth for shadow removal algorithms. This data set containing 186 images eliminates inconsistencies between shadow and shadow-free images and provides a range of different shadow types such as soft, textured, colored, and broken shadow. Using this data, the most thorough comparison of state-of-the-art shadow removal methods to date is performed, showing our proposed algorithm to outperform the state of the art across several measures and shadow categories. To complement our data set, an online shadow removal benchmark website is also presented to encourage future open comparisons in this challenging field of research.
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