Depth estimation from light field analysis based multiple cues fusion

Inspired by the depth perception mechanism of the human visual system, this paper proposes a novel globally consistent depth estimation method from defocus blur and stereo disparity depth-cues. According to the recent progress of light field theory, we can simulate the focus and defocus depth cue by using synthetic aperture photograph technique. Firstly, the defocus blur and stereo disparity depth cues are extracted from light field data sets, which is acquired by using a camera array system. Then based on the characteristic of the two depth cues, we design a fusion algorithm for the light field depth estimation. To acquire the globally consistent structural depth result, we introduce an adaptive weighted smoothing function in Markov random field framework. Finally, the global energy is minimized by graph cut algorithm, which leads a consistent and precise depth estimation. We test the proposed method on both virtual data and real data, the experimental results have shown that our method can take advantage of the two depth-cues and obtain more robust depth estimation.