Image matting based on mutual information
Xiaozhou Zhou, Pierre Boulanger · 2011
In this paper, we propose a novel framework to solve the image matting problem. We design a temporary image based on the estimated foreground and background colors for unknown pixels as well as an initial matte. The similarity of the temporary image and original image is modeled as an energy function in the Markov Random Field (MRF). The global optimized matte is obtained by minimizing the energy function. Therefore, image matting is converted to how to maximize the similarity of the original image and the temporary image. The experiments demonstrate that the proposed method could produce high quality mattes and it is also more effective compared to other top ranking methods.