A new multifocus image fusion based on spectrum comparison

In this paper, a spectrum comparison based multifocus image fusion algorithm is proposed. A distinctive feature of the proposed algorithm is that it constructs a global focus detection algorithm, which makes it get free of block artifacts and reduces the loss of contrast in the fused image. In this algorithm, source images are first transformed into Fourier space, in which we adopt the Bayesian prediction algorithm to smooth the log spectrum of each source image. By comparing the difference between the original log spectrum and its smoothed version, we can get the saliency region of each source image. Then image segmentation based on Sobel operator is employed to identify the smooth regions that may be affected by edges or textures, finally a sigmoid function is utilized to map the saliency comparison results to focus detection results in which affected smooth regions are treated in a different way. Experimental results demonstrate the superiority of the proposed method in terms of subjective and objective evaluation. Drawbacks of the existing image blocks selection methods are analyzed.A new focus detection method is proposed to reduce the lost of contrast.A sigmoid function is used in the fusion rule to make fused images more nature.The proposed algorithm holds for both gray-gray and color-color image fusion.

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