We propose an efficient disparity map enhancement method that improves the alignment of disparity edges and color edges even in the presence of mixed pixels and provides alpha values for pixels at disparity edges as a byproduct. In contrast to previous publications, the proposed method addresses mixed pixels at disparity edges and does not introduce mixed disparities that can lead to object deformations in synthesized views. The proposed algorithm computes transparencies by performing alpha matting per disparity-layer. These alpha values indicate the degree of affiliation to a disparity-layer and can hence be used as an indicator for a disparity reassignment that aligns disparity edges with color edges and accounts for mixed pixels. We demonstrate the capabilities of the proposed method on various images and corresponding disparity maps, including images that contain fuzzy object borders (e.g., fur). Furthermore, the proposed method is qualitatively and quantitatively evaluated using disparity ground truth and compared to previously published disparity post-processing methods.
N. Brosch, M. Nezveda, M. Gelautz, F. Seitner: "Efficient quality enhancement of disparity maps based on alpha matting"; Talk: SPIE Electronic Imaging Conference, San Francisco, California, USA; 02-02-2014 - 02-06-2014; in: "SPIE Electronic Imaging", (2014).
Nicole Brosch, Matej Nezveda, Margrit Gelautz, Florian Seitner, “Efficient quality enhancement of disparity maps based on alpha matting,” Proc. SPIE 9011, Stereoscopic Displays and Applications XXV, Andrew J. Woods; Nicolas S. Holliman; Gregg E. Favalora, 90110M (2014). http://dx.doi.org/10.1117/12.2035361
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