中文摘要：

针对同一场景的多聚焦图像融合问题,提出了一种新的基于提升静态小波变换（Lifting Stationary Wavelet Transform,LSWT）的多尺度积图像融合算法。该方法在选择融合图像的低频子带系数时定义了一种新的改进拉普拉斯能量和（Sum Modified-laplacian,SML）,设计了一种基于拉普拉斯能量和的加权与选择相结合系数选择方案;当选择高频子带系数时,根据多尺度积具有放大图像边缘特征弱化噪声的特点,在LSWT多尺度积的基础上给出了局部拉普拉斯算子和（Local Modified Laplacian,LML）的概念,并提出了基于多尺度积局部拉普拉斯算子和的系数选择方案;实验结果表明,该算法不仅能充分提取源图像信息注入到融合图像中,而且能有效抑制噪声的影响,得到比传统融合方法更优的视觉效果。

英文摘要：

For the fusion of multi-focus images of the same scene, a novel algorithm is proposed based on the multiscale products in lifting stationary wavelet domain. The selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed respectively. When choosing the low frequency subband coefficients, we present a scheme based on a new sum modified-laplacian combined with the selection and weighted scheme. When choosing the high frequency subband coefficients, local modified Laplacian is proposed based on multiscale products according to enhancing edges structure of multiscale products while weakening noise. Then, we present a selection principle based on the local modified Laplacian. The experiments show that the algorithms proposed in the paper can not only extract all the useful information of the source images and transfer to the fused images, but also effectively restrain the noise influence. Compared with the traditional methods, a better performance is obtained in terms of both visual qualities.