中文摘要：

现有压缩图像融合算法的采样模式以傅里叶谱域 内星形采样和小波谱域内放射状采样为主,并没有 充分利用信号在谱域内的结构化特征,算法的性能和效率仍有提升空间。为此,本文利用图 像在小波谱域内的重要变 换系数所体现出的结构化特征,通过沿小波谱域内子树结构进行自适应采样,以采集到图 像更多的重要 信息,并结合空域递归图像重构算法,提出一种新的压缩图像融合方法。数值实验结果表明 ,所提出的压 缩图像融合方法与现有的其它方法相比,不仅有效提高了图像融合效果,而且计算效率也有 很大提升。

英文摘要：

The compressed sensing （CS） theory has been introduced to the traditional i mage fusion procedure to improve its efficiency.However,the fusion image quality is strong ly affected by the sampling method which is used to generate the compressed coefficients.The e xisting compressed image fusion methods mainly utilize the star-shaped sampling in the Fourier fre quency domain and the radial sampling in the wavelet frequency domain.These sampling models ignor e the signal structure in the frequency field,which would readily cause a high sampling rate .Thus,it′s difficult to improve the efficiency and the performance of the current compresse d image fusion methods.In this work,the structure of the wavelet coefficients is investigated ,which indicates that the important wavelet coefficients form a quad-tree structure.Thus,a str uctured and self-adaptive sampling model is proposed for the compressed image fusion,by ta king advantage of the subtree structure which contains the important wavelet coefficients.The subtree sampling model can acquire more important coefficients because of the self-adaptive samp ling strategy. A new compressed image fusion algorithm is then proposed by combining the subtre e sampling model and the spatial recursive image reconstruction algorithm.Numerical results show that the proposed compressed image fusion algorithm can improve the image fusion performance and c omputational efficiency.