We propose a new method to address the problem of stereo matching under varying illumination conditions.
First, a spatially varying multiplicative model is developed to account for photometric changes induced between
both images in the stereo pair. The stereo matching problem based on this model is then formulated as a
constrained optimization problem in which an appropriate convex objective function is minimized under convex
constraints. These constraints arise from prior knowledge and rely on various properties of both disparity and
illumination fields. In order to obtain a smooth disparity field while preserving discontinuities around object
edges, we consider an appropriate wavelet-based regularization constraint. The resulting multi-constrained
optimization problem is solved via an efficient block iterative algorithm which offers great flexibility in the
incorporation of several constraints. Experimental results demonstrate the efficiency of the proposed method
to recover illumination changes and disparity map simultaneously, making stereo matching very robust w.r.t.