Edge-preserving disparity map estimation from stereo videos for bokeh synthesis

Abstract

We present a new method of estimating disparity maps from stereo videos for bokeh effect synthesis. In this work, we develop an improved total variation regularization and the robust L1 norm in the data fidelity term (TV-L1) [4] based method to estimate edge-preserving disparity map without stereo rectification. The proposed algorithm improves the TV-L1 approach by incorporating structure edge detection, occlusion area detection, textureless region detection and applying the guided filter to alleviate the inconsistency problem between the disparity map and color image around object boundary. Furthermore, we propose a temporal filter to improve the temporal consistency of the disparity maps computed from the stereo videos. We use saliency map to focus the synthesis result on the objects which attract human attention most. Experimental comparisons on various real videos are shown to demonstrate that the proposed algorithm generates more visually pleasing bokeh video synthesis compared with those by using previous stereo matching methods.