Traditional stereo algorithms estimate disparity at the same resolution as the observations. In this work we address the problem of estimating disparity and occlusion information at a higher resolution (HR). We draw on the image formation model from the motion superresolution domain to relate HR disparity and the observations. This approach estimates both the HR disparity and HR intensity. We minimize a suitably constructed cost function using graph cuts and iterated conditional modes (ICM) for disparity and intensity, respectively.
Arnav V. Bhavsar, A. N. Rajagopalan