Fast environment matting extraction using compressive sensing

Qi Duan, Jianfei Cai, Jianmin Zheng, Weisi Lin

Abstract: The existing high-accuracy environment matting extraction methods usually require the capturing of thousands of sample images and spend several hours in data acquisition. In this paper, a fast environment matting algorithm is proposed to ex tract the environment matte data effectively and efficiently. In particular, we incorporate the recently developed compressive sensing theory to simplify the data acquisition process. More over, taking into account special properties of light refraction and reflection effects of transparent object, we further propose to use hierarchical sampling and group clustering based recovery to accelerate the matte extraction process. Compared with the state-of-the-art approaches, our proposed algorithm significantly accelerates the environment matting extraction process while still achieving high-accuracy results.