A novel image fusion algorithm using wavelet transform multiscale edges detection is proposed in this paper. This wavelet analysis image fusion method is feature-based. The source images are fused using their multiscale edges information. In order to preserve fusion image edges better, the denoising and edges detection combined are statistical parameters is used to evaluate multifocus image fusion performance. The technique determining optimal decomposition level has explicit physical meaning in this paper. Comparing source images entropy with fusion image entropy at the same decomposition level, if arbitrary source image entropy is larger than the fusion image entropy, it is not neccesary to make next level decomposition. Experimental results demonstrate the effectiveness of the algorithm which increases entropy and standard deviation of fusion image.
何友,王国宏,陆大,彭应宁.多传感器信息融合与应用.北京:电子工业出版社,2000,第1章.[2]Hall D L, Llinas J. A introduction to multisensor data fusion[J].Proc. IEEE.1997, 85(1):6-[3]Li H, Manjunath B S, Mitra S K. Multisensor image fusion using the wavelet transform[J].Graphical Models and Image Processing.1995, 57(3):235-[4]Zhang Z, Blum R S. A categorization of multiscaledecomposition -based image fusion schemes with a performance study for a digital camera application[J].Proc. IEEE.1999, 87(8):1315-[5]Mallat S G, Zhong S F. Characterization of signals from multiscale edges. IEEE Trans. on PAMI, 1992, 14(7): 710 - 732.[6]Mallat S G. A Wavelet Tour of Signal Processing. San Diego:Academic Press, 1998: Chapter 6.