关键词: 小波变换, 广义高斯建模, 主成分分析, 参数估计, 多聚焦图像融合

Abstract:  For improving the performance of multi-focus image fusion and solving the problem that parameter estimation is short of accuracy in the statistical image fusion method, a novel multi-focus image fusion approach is proposed based on Principal Component Analysis（PCA） and generalized Gaussian modeling in wavelet transform domain. First, coefficients of the high frequency subbands are modeled as generalized Gaussian distribution, and the fusion parameters are estimated by an improved maximum likelihood method. Then, coefficients of the low frequency subbands are fused by a method of regional PCA. And finally, an effective fused image is achieved through inverse wavelet transform. The experimental results demonstrate that, comparing with existing multi-focus image fusion approaches, the proposed method makes the fused image have more information and better visual effect.

Key words: wavelet transform, generalized Gaussian modeling, principal component analysis, parameter estimation, multi-
focus image fusion