Guided filtering based data fusion for light field depth estimation with L0 gradient minimization

In this paper, we propose guided filtering based data fusion for light field depth estimation with L0 gradient minimization. Stereo disparity produces good depth edge, while defocus response yields good depth information in homogeneous regions. We fuse stereo disparity and defocus response from light filed data in a guided filtering framework. In the guided filtering framework, we adopt L0 gradient minimization as the regularization term instead of penalizing linear coefficients to consider depth characteristics that have similar depth in the same object. Moreover, we utilize edge direction in stereo matching to prevent the confusion caused by occlusion. Experimental results on both synthetic and real light field datasets show that the proposed method achieves clearer edge and less error in depth than state-of-the-arts.