Robust Light Field Depth Estimation with Occlusion Based on Spatial and Spectral Entropies Data Costs

This paper proposes a novel data cost that combines spatial and spectral entropies to handle the occlusion problem in the light field depth estimation. In previous works, the spatial entropy data cost has been demonstrated to reduce the effect of occluded pixels in an angular patch (i.e., micro-lens pixel) and to yield an accurate depth value in the presence of occlusion. However, our observation notes that the spatial entropy data cost metric is less reliable when the angular resolution becomes smaller as in light field images. In this paper, we propose a new data cost which integrates a proposed spectral entropy data cost with the spatial entropy data cost. An initial depth map which is estimated using the proposed new data cost is further optimized by the standard graph-cut algorithm and filtered by using an edge-preserving filter. Experimental results have confirmed the effectiveness of the proposed method which achieves more accurate depth values even when the angular resolution becomes smaller.