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This study presents a novel iterative algorithm of joint depth and alpha matte optimization via stereo (JDMOS). This algorithm realizes simultaneous estimation of depth map and matting image to obtain final convergence. The depth map provides depth information to realize automatic image matting, whereas the border details generated from the image matting can refine the depth map in boundary areas. Compared with monocular matting methods, another advantage offered by JDMOS is that the image matting process is completely automatic, and the result is significantly more robust when depth information is introduced. The major contribution of JDMOS is adding image matting information to the cost function, thereby refining the depth map, especially in the scene boundary. Similarly, optimized disparity information is stitched into the matting algorithm as prior knowledge to make the foreground–background segmentation more accurate. Experimental results on Middlebury datasets demonstrate the effectiveness of JDMOS.

Joint Depth and Alpha Matte Optimization via Stereo