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Multimodel fusion method via sparse representation at pixel-level and feature-level

机译：通过像素级和特征级的稀疏表示的多模型融合方法

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摘要

In current fusion methods based on sparse representation (SR) and different frequency, the SR is usually applied to fusion of the low-frequency components. In contrast, the direct fusion is usually adopted for high-frequency components due to their significant diversity. However, the effect of the direct fusion is degraded by the redundant information resulting from the correlation between original signals. A multimodel fusion framework is proposed by applying the SR to low-frequency fusion at pixel-level and high-frequency fusion at feature-level, respectively. First, the multimodal images are decomposed into high-frequency and low-frequency components by nonsubsampled contourlet transform (NSCT). Second, the universal high-frequency dictionary is constructed by using the fast independent component analysis (ICA) of the source high-frequency and its subband images. They represent the general feature part and unique feature part for the high-frequency signals, respectively. The universal low-frequency dictionary is constructed by using the original low-frequency signals. Third, the direct fusion of the high-frequency is converted into sparse coefficients fusion in fast ICA domain. Moreover, the multiple directive contrasts by modifying sum-modified Laplacian are taken as the fusion rule. The low-frequency signals are fused by using an activity measure based on weights. Finally, the fused image is obtained by inverse NSCT on the merged components. The experiments are conducted on three types of image pairs, and the results demonstrate that the proposed method outperforms seven state-of-the-art methods, in terms of four subjective and objective evaluations.
机译：在当前基于稀疏表示（SR）和不同频率的融合方法中，SR通常用于低频分量的融合。相反，由于高频分量的多样性，通常将直接融合用于高频分量。但是，直接融合的效果会因原始信号之间的相关性而导致的冗余信息而降低。通过将SR分别应用于像素级的低频融合和特征级的高频融合，提出了一种多模型融合框架。首先，通过非下采样轮廓波变换（NSCT）将多峰图像分解为高频和低频分量。其次，通过使用源高频及其子带图像的快速独立分量分析（ICA）构建通用高频字典。它们分别代表高频信号的一般特征部分和独特特征部分。通用低频字典是使用原始低频信号构建的。第三，将高频的直接融合转换为快速ICA域中的稀疏系数融合。此外，通过修改和修改的拉普拉斯算子的多指令对比被视为融合规则。通过使用基于权重的活动度量来融合低频信号。最后，通过逆NSCT对合并后的分量获得融合图像。在三种类型的图像对上进行了实验，结果表明，在四种主观和客观评估方面，所提出的方法优于七种最新方法。

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