We present a novel technique for shadow removal based on an information theoretic
approach to intrinsic image analysis. Our key observation is that any illumination
change in the scene tends to increase the entropy of observed texture intensities.
Similarly, the presence of texture in the scene increases the entropy of the
illumination function. Consequently, we formulate the separation of an image into
texture and illumination components as minimization of entropies of each component.
We employ a non-parametric kernel-based quadratic entropy formulation, and present
an efficient multi-scale iterative optimization algorithm for minimization of the
resulting energy functional. Our technique may be employed either fully
automatically, using a proposed learning based method for automatic initialization,
or alternatively with small amount of user interaction. As we demonstrate, our
method is particularly suitable for aerial images, which consist of either
distinctive texture patterns, e.g. building facades, or soft shadows with large
diffuse regions, e.g. cloud shadows.