Practical background estimation for mosaic blending with patch-based Markov random fields

In this paper, we present a new background estimation algorithm which effectively represents both background and foreground. The problem is formulated with a labeling problem over a patch-based Markov random field (MRF) and solved with a graph-cuts algorithm. Our method is applied to the problem of mosaic blending considering the moving objects and exposure variations of rotating and zooming camera. Also, to reduce seams in the estimated boundaries, we propose a simple exposure correction algorithm using intensities near the estimated boundaries.