Focus Fusion with Anisotropic Depth Map Smoothing

Abstract

Abstract. Focus fusion methods combine a set of images focused at different depths into a single image where all parts are in focus. The quality of the fusion result strongly depends on a decision map that determines the in-focus areas. Most approaches in the literature achieve this by local decisions without explicitly enforcing smoothness of the depth map. The goal of our paper is to introduce a modern regularisation strategy where we assume that neighbouring pixels in the resulting image have a similar depth. To this end, we consider a partial differential equation (PDE) for the depth map. It combines a robustified data fidelity term with an anisotropic diffusion strategy that involves a matrix-valued diffusion tensor. Experiments with synthetic and real-world data show that this depth map regularisation can improve existing fusion methods substantially. Our methodology is general and can be applied to improve many existing fusion methods.

A focus fusion framework with anisotropic depth map smoothing

“…In our approach, we formulate a similarity to a precomputed depth map or even to a composite of multiple depth maps by a robust data term and combine it with a modern adaptive regularisation technique: Our joint imageand depth-driven diffusion is guided by the structures of the evolving all-infocus image, while the amount of smoothing is determined by the depth map gradients. In the present paper, we extend our conference publication [22] in several aspects:…”

“…(i) In [22], we applied the gradient magnitude as indicator of sharp image regions. However, our method is very general and not limited to this specific choice: It creates a high quality depth map using one or multiple depth maps that can been precomputed with various sharpness measures.…”