Block-based winner-takes-all reconstruction of intermediate stereoscopic images

This paper addresses the issue of the reconstruction of intermediate views from a pair of stereoscopic images. Such a reconstruction is needed for the enhancement of depth perception in stereoscopic systems, e.g., 'continuous look around' or adjustment of virtual camera baseline. The algorithm proposed here addresses the issue of blue; unlike typical reconstruction algorithms that perform averaging between disparity-compensated left and right images the new algorithm uses non-linear filtering via a winner-takes-all strategy. The image under reconstruction is assumed to be a tiling by fixed-size blocks that come from various positions of either the left or right images using disparity compensation. The tiling map is modeled by a binary decision field while the disparity model is based on a smoothness constraint. The models are combined through a maximum a posteriori probability criterion. The intermediate intensities, disparities and the binary decision field are estimated jointly using the expectation-maximization algorithm. The proposed algorithm is compared experimentally with a reference block-based algorithm employing linear filtering. Although the improvements are localized and often subtle, they demonstrate that a high-quality intermediate view reconstruction for complex scenes is feasible if camera convergence angle is small.