Depth estimation by analyzing intensity distribution for light-field cameras

In this paper, a novel depth estimation algorithm is proposed for light-field cameras by fully exploiting the characteristics of 4D light-field data. Based on a standard light-field acquisition system, a novel tensor, intensity range of pixels within a microlens, is proposed, which presents much stronger correlation with the transition on focus. Then, the tendency of intensity range is combined with the texture gradient to further improve the accuracy and consistency of the estimated depth and preserve edges through global optimization. Compared with the representative approaches, the depth generated by the proposed approach presents clearer object boundaries with much richer details both for indoor and outdoor contents. Moreover, the execution time is on average 12 times lower than the existing approaches.