Wavelet-constrained regularization for disparity map estimation

This paper describes a novel method for estimating dense disparity maps, based on wavelet representations. Within the proposed set theoretic framework, the stereo matching problem is formulated as a constrained optimization problem in which a quadratic objective function is minimized under multiple convex constraints. These constraints arise from the prior knowledge and the observations. In order to obtain a smooth disparity field, while preserving edges, we consider appropriate wavelet based regularization constraints. The resulting optimization problem is solved with a block iterative method which offers great flexibility in the incorporation of several constraints. Experimental results on both synthetic and real data sets show the excellent performance and robustness w.r.t. noise of our method.