Multifocus image fusion based on surface area analysis

Multifocus image fusion is an important research area in image processing and machine vision applications. Due to use of optical lenses, captured images are not usually focused everywhere in the image. Therefore objects near the focal range have evident details while other objects appear blurry. Multifocus image fusion algorithm takes several images with different focal ranges and combines them to produce an image that is focused everywhere. To identify focused regions in each of input images, generally spatial domain and transform domain methods are used. These methods usually suffer from artifacts such as blockiness or ringing. In this paper we propose a new criteria to determine focused pixels in an image. We find the points that have the same intensities in input images and segment the input images based on them. Subsequently we calculate surface area of pixels inside every segment based on intensity variations over rows and columns. Segment with more surface area of input images selected as the focus segment. Experimental results reveal the superiority of our method in comparison to compared algorithms.