Intrinsic Image Decomposition Using Optimization and User Scribbles
In this paper, we present a novel high-quality intrinsic image recovery approach using optimization and user scribbles. Our approach is based on the assumption of color characteristics in a local window in natural images. Our method adopts a premise that neighboring pixels in a local window having similar intensity values should have similar reflectance values. Thus, the intrinsic image decomposition is formulated by minimizing an energy function with the addition of a weighting constraint to the local image properties. In order to improve the intrinsic image decomposition results, we further specify local constraint cues by integrating the user strokes in our energy formulation, including constant-reflectance, constant-illumination, and fixed-illumination brushes. Our experimental results demonstrate that the proposed approach achieves a better recovery result of intrinsic reflectance and illumination components than the previous approaches.