基于视觉显著性加权与梯度奇异值最大的红外与可见光图像融合 -

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基于视觉显著性加权与梯度奇异值最大的红外与可见光图像融合

doi:10.37188/CO.2022-0124

中国空间技术研究院遥感卫星总体部, 北京 100094

基金项目:国家重大航天工程

详细信息

作者简介:
程博阳（1992—），男，北京人，博士，中国空间技术研究院遥感卫星总体部工程师。2015年于吉林大学获得理学学士学位，2020年于中国科学院大学获得工学博士学位，主要从事空间遥感相机总体设计与图像融合工作。E-mail：boyangwudi@163.com

中图分类号:TP394.1;TH691.9
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出版历程
- 收稿日期:2022-06-13
- 修回日期:2022-06-29
- 网络出版日期:2022-06-29

Fusion of infrared and visible light images based on visual saliency weighting and maximum gradient singular value

Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China

Funds:Supported by National Major Aerospace Project

More Information

Corresponding author:boyangwudi@163.com

摘要

摘要:
为了综合利用红外与可见光图像的光谱显著性信息，同时提高融合图像的视觉对比度，本文提出了一种基于视觉显著性加权与梯度奇异值最大的红外与可见光图像融合方法。首先，该全新算法通过滚动引导剪切波变换作为多尺度分析工具，来获取图像的近似层分量与多方向细节层分量。其次，针对反映图像主体能量特征的近似层分量，采用视觉显著性加权融合作为其融合规则，该方法利用显著性加权系数矩阵指导图像内的光谱显著性信息有效融合，提高了融合图像的视觉观察度。此外，采用基于梯度奇异值最大原则来指导细节层分量的融合，该方法可以极大程度地将隐藏在两种源图像内的梯度特征还原到融合图像中，使融合图像具有更加清晰的边缘细节。为了验证本文算法的有效性，进行了5组独立的融合实验，最终的实验结果表明，本文算法融合图像的对比度更高，边缘细节更加丰富，并且相较于其它现有典型方法，AVG、IE、Q^E、SF、SD、SCD等客观参数指标分别提高了16.4%、3.9%、11.8%、17.1%、21.4%、10.1%，因此具有更加优良的视觉效果。
- 图像融合/
- 滚动引导剪切波变换/
- 显著性加权/
- 梯度奇异值
Abstract:
In order to effectively integrate the spectral saliency information of infrared and visible light images and improve the visual contrast of the fused images, a fusion method of infrared and visible light images based on weighted visual saliency and maximum gradient singular value is proposed in this paper. Firstly, the new algorithm uses the rolling guidance shearlet transform as a multi-scale analysis tool to obtain the approximate layer components and multi-directional detail layer components of the image. Secondly, for the approximate layer components that reflect the energy characteristics of the image subject, visual saliency weighted fusion is used as its fusion rule. This method uses the saliency weighted coefficient matrix to guide the effective fusion of spectral saliency information in the image, and improves the visual observation of the fused image. In addition, the principle of maximum gradient singular value is used to guide the fusion of detail layer components. This method can restore the gradient features hidden in the two source images to the fused image to a great extent, so that the fused image has clearer edge details. In order to verify the effectiveness of this algorithm, we have adopted five groups of independent fusion experiments. The final experimental results show that this algorithm has higher contrast and richer edge details. Compared with the existing typical methods, the objective parameters such as AVG, IE, Q^E, SF, SD and SCD are improved by 16.4%, 3.9%, 11.8%, 17.1%, 21.4% and 10.1%, respectively, so it has better visual effect.
- image fusion/
- rolling guidance shearlet transform/
- visual saliency weighted/
- gradient singular value

HTML全文

图 1基于MS-RGF分解后的多尺度图像

Figure 1.Multi-scale images decomposed based on MS-RGF

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图 2L=8的伪极化坐标网络

Figure 2.Pseudo-polar coordinate network withL= 8

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图 3剪切波在频域的滤波器组

Figure 3.The filter bank of shearlet in frequency domain

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图 4多方向剪切波变换的效果图

Figure 4.Effect diagrams of multi-directional shearlet transform

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图 5RGST的分解与重构示意图

Figure 5.Schematic diagram of decomposition and reconstruction of RGST

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图 6本文融合算法示意图

Figure 6.Schematic diagram of the fusion algorithm in this paper

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图 7融合实验采用的红外与可见光图像

Figure 7.Infrared and visible light images used in the fusion experiment

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图 8不同分解级数下的AVG值比较

Figure 8.Comparison of AVG values under different decomposition levels

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图 9不同分解级数下的IE值比较

Figure 9.Comparison of IE values under different decomposition levels

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图 10第一组图像融合实验结果

Figure 10.The first group of image fusion experiment

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图 11第二组图像融合实验结果

Figure 11.The second group of image fusion experiment

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图 12第三组图像融合实验

Figure 12.The third group of image fusion experiment

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图 13第四组图像融合实验

Figure 13.The fourth group of image fusion experiment

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图 14第五组图像融合实验

Figure 14.The fifth group of image fusion experiment

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表 1第1组图像融合实验的客观评价指标

Table 1.Objective evaluation indicators for the first group of image fusion experiments

第1组融合实验	评价指标
第1组融合实验	AVG	IE	Q^E	SF	SD	SCD	t
CVT	10.59	7.10	0.58	18.88	35.67	1.54	3.93
NSCT	6.42	7.51	0.45	11.41	47.22	1.59	109.8
ADF	10.22	6.91	0.53	17.64	30.76	1.51	2.07
WLS	11.14	7.14	0.398	20.38	41.19	1.74	4.18
MSVD	9.36	6.84	0.37	16.63	29.26	1.52	0.76
TSF	9.61	7.27	0.56	17.76	40.58	1.68	0.13
本文方法	11.44	7.42	0.62	20.65	47.66	1.78	8.82

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表 2第2组图像融合实验的客观评价指标

Table 2.Objective evaluation indicators for the second group of image fusion experiments

第2组融合实验	评价指标
第2组融合实验	AVG	IE	Q^E	SF	SD	SCD	t
CVT	8.76	7.05	0.58	21.67	33.65	1.51	1.81
NSCT	5.73	7.17	0.42	12.15	37.88	1.20	65.1
ADF	7.55	6.83	0.50	17.18	28.28	1.50	1.25
WLS	8.88	7.06	0.46	20.88	33.54	1.65	2.36
MSVD	7.84	6.83	0.46	19.75	28.42	1.54	0.35
TSF	7.68	7.11	0.55	19.40	35.16	1.58	0.13
本文方法	9.44	7.26	0.62	22.95	40.11	1.65	4.64

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表 3第3组图像融合实验的客观评价指标

Table 3.Objective evaluation indicators for the third group of image fusion experiments

第3组融合实验	评价指标
第3组融合实验	AVG	IE	Q^E	SF	SD	SCD	t
CVT	4.98	6.91	0.59	14.80	34.32	1.60	2.22
NSCT	4.13	7.37	0.54	9.78	50.49	1.62	91.1
ADF	3.03	6.62	0.41	8.88	28.99	1.52	1.42
WLS	5.11	7.10	0.55	15.47	47.80	1.81	3.16
MSVD	3.95	6.65	0.46	11.99	29.52	1.53	0.45
TSF	4.918	7.08	0.63	14.93	39.02	1.70	0.14
本文方法	5.75	7.15	0.65	16.39	48.65	1.82	7.51

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表 4第4组图像融合实验的客观评价指标

Table 4.Objective evaluation indicators for the fourth group of image fusion experiments

第4组融合实验	评价指标
第4组融合实验	AVG	IE	Q^E	SF	SD	SCD	t
CVT	9.18	6.91	0.39	17.27	33.98	1.48	1.34
NSCT	6.06	7.18	0.31	11.15	38.07	1.21	29.46
ADF	5.37	6.62	0.34	10.10	27.90	1.46	0.90
WLS	9.82	6.96	0.39	17.99	34.19	1.58	1.29
MSVD	7.94	6.66	0.32	14.57	28.34	1.45	0.18
TSF	8.13	7.04	0.43	16.82	37.05	1.63	0.11
本文方法	9.84	7.15	0.43	18.42	39.04	1.68	2.44

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表 5第5组图像融合实验的客观评价指标

Table 5.Objective evaluation indicators for the fifth group of image fusion experiments

第5组融合实验	评价指标
第5组融合实验	AVG	IE	Q^E	SF	SD	SCD	t
CVT	12.25	7.54	0.50	24.83	46.91	1.75	2.25
NSCT	9.75	7.81	0.43	18.79	55.87	1.64	53.80
ADF	9.19	6.97	0.42	17.96	32.86	1.74	1.33
WLS	12.53	7.35	0.38	24.62	43.74	1.87	2.64
MSVD	10.66	6.99	0.43	22.59	33.35	1.78	0.32
TSF	12,00	7.68	0.53	25.74	52.17	1.84	0.15
本文方法	14.31	7.76	0.57	28.76	57.92	1.89	3.42

下载: 导出CSV

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