静态小波域内特征对比度多聚焦图像融合算法

Multifocus image fusion scheme based on feature contrast in lifting stationary wavelet domain

针对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)的多聚焦图像融合方法。对经LSWT分解得到的不同频域子带系数采用不同的系数选择方案。在融合低频子带系数时考虑到人眼视觉对图像局部对比度比较敏感的特性,引入了一种新的局部特征对比度的概念,并给出了低频子带系数的选择方案。在融合高频子带系数时,充分考虑到人眼视觉对图像边缘细节比较敏感的特性而对单个像素的亮度不敏感的特性,引入了一种适应于高频子带系数的特征对比度的概念,设计出一种基于特征对比度的系数选择方案。实验证明,算法相对于传统的基于图像对比度的图像融合方法,能够提取更多的有用信息并注入到融合图像中,得到视觉效果更好,更优量化指标的融合图像。

A novel multifocus image fusion method based on lifting stationary wavelet transform （LSWT） is proposed. The selection principles, namely fusion rules of different subband coefficients, are discussed in detail. Local feature contrast is presented according to the human vision system （HVS）, which is highly sensitive to the local image contrast level. Then, the fusion rule for the low-frequency subband coefficients fusion is introduced. To choose the high frequency subband coefficients, another local feature contrast is developed according to the human vision which is often sensitive to edges and directional features, but insensitive to real luminance at independent positions. Then, a novel fusion rule is proposed for fusion of the high frequency subband coefficients. Experimental results demonstrate that the proposed image fusion method is effective and can provide better performance in fusing multifocus images than the traditional contrast-based image fusion algorithms in term of informal visual inspection and objective criteria in multifocus image fusion.