Image Fusion of Infrared and Visible Images Based on Residual Significance
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摘要: 为了将可见光图像与红外图像中的细节信息更多的呈现在融合图像中，突出目标特征并获得更好的图像视觉效果，本文提出一种基于改进谱残差显著性图的红外与可见光图像融合方法。首先用改进的谱残差显著性检测算法提取红外图像的显著性图并获得融合图像的显著性系数，然后对源图像进行双树复小波分解，并根据特定的融合规则分别对图像的低频部分以及高频部分进行融合，最后采用双树复小波逆变换重构获得最终的融合图像。实验表明，本文融合方法相较于传统融合方法融合质量更高并且在视觉效果上有显著提升。Abstract: To make the fusion image show more image details and to obtain a better image visual effect, a fusion method based on residual significance is proposed. First, the infrared image is analyzed using residual significance to obtain its significance coefficients. Then, the source images are decomposed using a dual-tree complex wavelet transform, and the low- and high-frequency components are fused according to different fusion rules. Finally, the fusion image is reconstructed using the inverse transformation of a dual-tree complex wavelet. Experimental results showed that the fusion method proposed in this paper produced higher quality images and better visual effects than those of the traditional fusion method.
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表 1 融合图像客观评价结果
Table 1. The objective evaluation results of fused images
Scene Fusion method IE SD SF Qc MI Scene 1 LP 5.2477 25.0859 6.4781 0.6714 2.6909 DWT 5.9016 20.5764 8.4659 0.7026 3.0138 GFNSCT 5.9431 25.5093 12.0113 0.7223 3.1173 Ours 5.9672 36.6538 12.6746 0.8148 3.1984 Scene 2 LP 7.2489 44.5980 10.5057 0.7089 2.1191 DWT 7.0574 40.5774 12.7919 0.6914 2.2907 GFNSCT 7.1928 42.3394 15.7901 0.7218 2.3410 Ours 7.2985 46.4514 15.9041 0.7901 2.3667 Scene 3 LP 6.5528 26.4718 12.2714 0.7551 3.0173 DWT 6.4503 25.7847 14.1124 0.8081 3.4493 GFNSCT 6.6167 32.1633 16.5527 0.8271 3.5117 Ours 7.0221 37.5441 16.9096 0.8650 3.6869 
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