A focus fusion framework with anisotropic depth map smoothing

• We show that depth map regularisation is an important tool for focus fusion.
• We incorporate modern concepts such as a coupled anisotropic diffusion term.
• We substantially improve the runtime with a fast GPU implementation.
• We evaluate different in-focus measures.
• We compare the overall performance to several methods from the literature.

Focus fusion is the task of combining a set of images focused at different depths into a single image that is entirely in-focus. The crucial point of all focus fusion methods is the decision about the in-focus areas. To this end, we present a general framework for focus fusion that introduces a modern regularisation strategy on these per-pixel decisions. We assume that neighbouring pixels in the fused image belong to similar depth layers. Following this assumption, we smooth the depth map with a sophisticated anisotropic diffusion process combined with a robust data fidelity term. The experiments with synthetic and real-world data demonstrate that our new model yields a better quality than several existing focus fusion methods. Moreover, our methodology is general and can be applied to improve many fusion approaches.