Fast shadow removal using adaptive multi-scale illumination transfer

In this paper, we present a new method for removing shadows from images. First, shadows are detected by interactive brushing assisted with a Gaussian Mixture Model. Secondly, the detected shadows are removed using an adaptive illumination transfer approach that accounts for the reflectance variation of the image texture. The contrast and noise levels of the result are then improved with a multi-scale illumination transfer technique. Finally, any visible shadow boundaries in the image can be eliminated based on our Bayesian framework. We also extend our method to video data and achieve temporally consistent shadow-free results. In this paper, we present a new method for removing shadows from images. First, shadows are detected by interactive brushing assisted with a Gaussian Mixture Model. Second, the detected shadows are removed using an adaptive illumination transfer approach that accounts for the reflectance variation of the image texture. The contrast and noise levels of the result are then improved with a multi-scale illumination transfer technique. Finally, any visible shadow boundaries in the image can be eliminated based on our Bayesian framework. We also extend our method to video data and achieve temporally consistent shadow free results.