Depth-based focus stacking with labeled-laplacian propagation

Abstract

Focus stacking is a promising technique to extend the depth of field in general photography, through fusing different images focused at various depth plane. However, existing depth propagation process in depth-based focus stacking is affected by colored texture and structure differences in guided images. In this paper, we propose a novel focus stacking method based on max-gradient flow and labeled Laplacian depth propagation. We firstly extract sparse source points with max-gradient flow to remove false edges caused in large blur kernel cases. Secondly, we present a depth-edge operator to give these sparse points 2 different labels: off-plane edges and in-plane edges. Only off-plane edges are then utilized in our proposed labeled-Laplacian propagation method to refine final dense depthmap and the all-in-focus image. Experiments show that our all-in-focus image is superior to other state-of-the-art methods.