Multi-focus image fusion with dense SIFT

Abstract

Multi-focus image fusion technique is an important approach to obtain a composite image with all objects in focus. The key point of multi-focus image fusion is to develop an effective activity level measurement to evaluate the clarity of source images. This paper proposes a novel image fusion method for multi-focus images with dense scale invariant feature transform (SIFT). The main novelty of this work is that it shows the great potential of image local features such as the dense SIFT used for image fusion. Particularly, the local feature descriptor can not only be employed as the activity level measurement, but also be used to match the mis-registered pixels between multiple source images to improve the quality of the fused image. In our algorithm, via the sliding window technique, the dense SIFT descriptor is first used to measure the activity level of source image patches to obtain an initial decision map, and then the decision map is refined with feature matching and local focus measure comparison. Experimental results demonstrate that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both subjective visual perception and objective evaluation metrics.