Abstract
In this paper, we propose a statistical scheme to judge the activity level measurement (ALM) that is based on wavelet-domain hidden Markov model (WD-HMM) and maximum likelihood (MLK). The source images are firstly decomposed by the wavelets and only the coefficients in the high frequency (HH) are utilized. Considering the shift-variance of wavelets, the merged image is obtained from the source images directly. The regions of each source image are obtained by the Hough transform (HT) and their ALM are decided by the ALM of their coefficients in HH according to MLK. Finally, two multi focus images are merged by our new framework. The fusion results show the high ability of our scheme in preserving edge information and avoiding shift-variant.
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Liao, Z.W., Hu, S.X., Chen, W.F., Tang, Y.Y., Huang, T.Z. (2006). A Statistical Image Fusion Scheme for Multi Focus Applications. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_115
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DOI: https://doi.org/10.1007/11739685_115
Publisher Name: Springer, Berlin, Heidelberg
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