Mean shift-based Bayesian image reconstruction into visual subspace

We present a new robust algorithm for reconstructing images into a linear subspace using MAP estimation. The algorithm takes into account the a priori distribution of the subspace variables and the noise is robustly modeled to allow for occlusions. The subspace distribution is estimated using nonparametric density estimation techniques. An efficient optimization scheme based on the mean shift procedure D Comaniciu et al. (2002) and on half-quadratic theory [ D Geman et al. (1992), P Charbonnier et al. (1997)] is developed, making optimization of the MAP function feasible for high-dimensional images. Preliminary results on real images demonstrate the contribution of a priori distribution modeling of sub-space variables, with respect to standard reconstruction methods over linear subspaces.