Abstract
Image fusion is a technique of combining information from multiple images of the same scene into an image, so that the fused image contains a more accurate description of the scene than any of the individual source images. SVT (Support Value Transform) method is one of leading multiscale methods for studying image fusion, which achieves better fusion results both in visual inspection and quantitative analysis. One important assumption of SVT method is representing salient feature of image by support value. However, related studies did not give any mathematical explanation of it. Another drawback of this fusion method is ignoring the rich directional information of original image. In this paper, we provide a rational evidence of the physical meaning of support value by introducing Gaussian curvature of image. Also, by combining with DFB (Directional Filter Bank), a multi-directional SVT image fusion method is proposed. Our method can capture different and flexible directional information, which may help obtain the intrinsic geometrical structure of original image. Experimental results on several pairs of multi-focus images show that the proposed method achieves better results than SVT and other traditional common used methods.
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