  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 non-uniform shadow
illumination changes, resulting in accurate and robust removal. Another major
contribution of this work is the first validated and multi-scene 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 new algorithm
to outperform the state-of-the-art across several measures and shadow category.
To complement our dataset, an online shadow removal benchmark website is also
presented to encourage future open comparisons in this challenging field of
research.
