Local matting based on sample-pair propagation and iterative refinement

This paper proposes a novel local matting algorithm based on sample-pair propagation and iterative refinement. Since sample-pairs of the foreground and background in the neighborhood are limited, they fail to fit the linear model well. We propose a sample-pair propagation scheme which propagates the confident sample-pair of each pixel to its neighbors so that they can collect more confident sample-pairs to estimate alpha values accurately. To avoid high time and space complexity of the global optimization, we convert matting into a de-noising problem and refine alpha values via fitting the linear model and smoothing the alpha matte locally and iteratively. Experimental results demonstrate that our algorithm produces more accurate results than the state-of-the-art of local matting.