IEEE Trans Image Process. 2012 May;21(5):2898-909. doi: 10.1109/TIP.2012.2183140. Epub 2012 Jan 9.
A novel higher order singular value decomposition (HOSVD)-based image fusion algorithm is proposed. The key points are given as follows: 1) Since image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor; 2) the sum of absolute values of the coefficients (SAVC) from HOSVD of subtensors is employed for activity-level measurement to evaluate the quality of the related image patch; and 3) a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion approach.
提出了一种新的基于高阶奇异值分解（HOSVD）的图像融合算法。要点如下：1）由于图像融合依赖于源图像的局部信息，因此该算法通过处理划分的子张量而不是整个张量来选择源图像的信息丰富的图像块来构成融合图像；2）子张量的 HOSVD 的系数的绝对值之和（SAVC）用于活动级别的测量，以评估相关图像块的质量；3）应用了一种新的类 sigmoid 函数的系数组合方案来构建融合结果。实验结果表明，该算法是一种替代的图像融合方法。
