Abstract
In multi-focus image fusion, the aim is to create a single image where the whole scene is focused by fusing multiple images captured with different focus distances. The fused image has greater depth of field than each of the input images. In this paper, we present a new method for multi-focus image fusion via random walks on graphs. The proposed method first evaluates the focus areas in a local sense and identifies nodes corresponding to consistency of nodes in a global sense. Several popular feature sets based on focus measure and color consistency are evaluated and employed to create a fully connected graph to model the global and local characteristics, respectively, of the random walks. The behavior of random walks on the graph is utilized to compute the weighting factor for each of the shallow depth-of-field input image. Experimental results show that the proposed method outperforms many state-of-the-art techniques in both subjective and objective image quality measures.
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