A Confidence-Aware Depth Estimation Method For Light-Field Cameras Based On Multiple Cues

Depth map estimation from a light-field camera is an interesting and challenging problem. Recent works have demonstrated many fascinating results based on different cues in light-field images. According to the characteristics of light-field spatial refocusing, we introduced a confidence - aware depth estimation method on the basis of multiple cues. In this paper, the focus/defocus cue of focal stack is estimated in Discrete Cosine Transform (DCT) domain. Based on photo-consistency metric and relevance analysis, the correspondence cue between different rays of a refocusing pixel is extracted. Then the edge confidence analysis is introduced as the depth and color discontinuity cues. In order to get refined depth map, an iterative graph cut optimization framework with label cost is used to integrate these aforementioned cues with their confidences. Experimental results showed that our method can achieve accurate depth maps, especially in the depth discontinuous areas.