Block-Based MAP Disparity Estimation Under Alpha-Channel Constraints

Disparity estimation belongs to the most important, but difficult, problems in image processing and computer vision. Its importance stems from a wide range of applications, while its difficulty is related to ill-posedness. To date, numerous disparity estimation algorithms have been developed. In this paper, we consider a particular case of disparity estimation based on two views and a known alpha channel partitioning each view into foreground and background. The main idea is to use this partitioning in order to enhance disparity estimation in the foreground object close to its boundary. We propose a block-based disparity model with two alpha-channel constraints: a photometric one, disabling invalid intensity/color matches, and a geometric one, preventing disparity smoothing between foreground and background. We incorporate these constraints into a Bayesian framework using the maximum a posteriori probability criterion. We experimentally demonstrate improvements in the estimated disparities at foreground object boundaries, and show examples of image relighting using these disparities