  A major challenge for matching-based depth estimation is to prevent
mismatches in occlusion and smooth regions. An effective matching window
satisfying three characteristics: texture richness, disparity consistency and
anti-occlusion should be able to prevent mismatches to some extent. According
to these characteristics, we propose matching entropy in the spatial domain of
light field to measure the amount of correct information in a matching window,
which provides the criterion for matching window selection. Based on matching
entropy regularization, we establish an optimization model for depth estimation
with a matching cost fidelity term. To find the optimum, we propose a two-step
adaptive matching algorithm. First, the region type is adaptively determined to
identify occluding, occluded, smooth and textured regions. Then, the matching
entropy criterion is used to adaptively select the size and shape of matching
windows, as well as the visible viewpoints. The two-step process can reduce
mismatches and redundant calculations by selecting effective matching windows.
The experimental results on synthetic and real data show that the proposed
method can effectively improve the accuracy of depth estimation in occlusion
and smooth regions and has strong robustness for different noise levels.
Therefore, high-precision depth estimation from 4D light field data is
achieved.
