A focus fusion framework with anisotropic depth map smoothing. Issue 11 (November 2015)

• Record Type: Journal Article
• Authors: Boshtayeva, Madina; Hafner, David; Weickert, Joachim
• Is Part Of: Pattern recognition. Volume 48:Issue 11(2015:Nov.)
• Page Start: 3310
• Page End: 3323
• Publication Date: 2015-11
• Subjects: Focus fusion -- In-focus measure -- Depth map -- Anisotropic smoothness; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2014.10.008
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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. Abstract : Highlights: 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.