Disparity map restoration by integration of confidence in Markov random fields models
This paper proposes some Markov random field (MRF) models for the restoration of stereo disparity maps. The main aspect is the use of confidence maps provided by the symmetric multiple windows (SMW) stereo algorithm to guide the restoration process. The SMW algorithm is an adaptive, multiple-window scheme using left-right consistency to compute disparity and its associated confidence in the presence of occlusions. The MRF approach allows the combining in a single functional of all the available information: observed data with its confidence, noise, and a-priori hypotheses. Optimal estimates of the disparity are obtained by minimizing an energy functional using simulated annealing. Results with a real stereo pair show the improvement obtained by restoration using the MRF approach integrating confidence data.