Night-vision Image Fusion Based on Intensity Transformation and Two-scale Decomposition
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摘要:
为了获得更适合人感知的夜视融合图像，该文提出一种基于灰度变换与两尺度分解的夜视图像融合算法。首先，利用红外像素值作为指数因子对可见光图像进行灰度转换，在达到可见光图像增强的同时还使可见光与红外图像融合任务转换为同类图像融合。其次，通过均值滤波对增强结果与原始可见光图像进行两尺度分解。再次，运用基于视觉权重图的方法融合细节层。最后，综合这些结果重构出融合图像。由于该文方法在可见光波段显示结果，因此融合图像更适合视觉感知。实验结果表明，所提方法在视觉质量和客观评价方面优于其它5种对比方法，融合时间小于0.2 s，满足实时性要求。融合后图像背景细节信息清晰，热目标突出，同时降低处理时间。
Abstract:In order to achieve more suitable night vision fusion images for human perception, a novel night-vision image fusion algorithm is proposed based on intensity transformation and two-scale decomposition. Firstly, the pixel value from the infrared image is used as the exponential factor to achieve intensity transformation of the visible image, so that the task of infrared-visible image fusion can be transformed into the merging of homogeneous images. Secondly, the enhanced result and the original visible image are decomposed into base and detail layers through a simple average filter. Thirdly, the detail layers are fused by the visual weight maps. Finally, the fused image is reconstructed by synthesizing these results. The fused image is more suitable for the visual perception, because the proposed method presents the result in the visual spectrum band. Experimental results show that the proposed method outperforms obviously the other five methods. In addition, the computation time of the proposed method is less than 0.2 s, which meet the real-time requirements. In the fused result, the details of the background are clear while the objects with high temperature variance are highlighted as well.
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表 1 不同融合方法的客观性能指标
图像 评价指标 LAP ROLP CVT DTCWT ADF 本文方法 $\mathop \mu \limits^ \wedge $ 52.5067 55.5025 51.9005 51.8983 51.7756 70.1690 Quad $\sigma $ 31.5616 28.2624 25.1804 25.2682 21.9894 34.3756 ${E_f}$ 6.4729 6.1093 6.1692 6.1586 6.0398 6.7689 $\mathop \mu \limits^ \wedge $ 90.8149 96.3052 91.0868 91.0788 91.1387 124.2739 UNcamp $\sigma $ 29.1292 27.7301 26.9391 26.2760 23.2265 38.3262 ${E_f}$ 6.6550 6.5508 6.5310 6.4847 6.2865 7.2638 $\mathop \mu \limits^ \wedge $ 82.1788 86.1979 82.1010 82.0766 82.0353 122.6444 Kaptein $\sigma $ 36.2649 35.7918 34.1582 33.6152 31.6902 51.6181 ${E_f}$ 6.7763 6.7911 6.7779 6.7054 6.6047 7.4176 $\mathop \mu \limits^ \wedge $ 110.9204 113.3709 110.9161 110.9148 110.9183 163.6281 Steamboat $\sigma $ 14.0743 13.8319 12.4700 12.3160 11.0786 26.4028 ${E_f}$ 5.3071 5.3595 5.2087 5.1377 5.0049 5.9645 
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表 2 处理时间对比(s)
图像 大小 LAP ROLP CVT DTCWT ADF 本文方法 Quad 496×632 0.0193 0.1931 1.9994 0.5288 0.9267 0.1681 UNcamp 270×360 0.0094 0.1076 1.2281 0.2480 0.3225 0.1021 Kaptein 450×620 0.0203 0.1919 1.8308 0.4891 0.8570 0.1341 Steamboat 510×505 0.0127 0.1771 1.7049 0.4434 0.8472 0.1192 平均 0.0247 0.1674 1.6908 0.4273 0.7384 0.1309
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