基于NSCT结合显著图与区域能量的红外与可见光图像融合

牛振华; 邢延超; 林英超; 王晨轩

基于NSCT结合显著图与区域能量的红外与可见光图像融合

青岛理工大学信息与控制工程学院, 山东青岛 266520

基金项目:

山东省自然科学基金 ZR2021MF101

详细信息

作者简介:
牛振华（1996-），男，硕士研究生，主要从事图像处理方面的研究。E-mail: 2215176247@qq.com

通讯作者:
邢延超（1973-），男，博士，副教授，研究方向为数字信号处理，水声通信技术。E-mail：9609891@qq.com

中图分类号: V271.4;TP391
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出版历程
- 收稿日期: 2022-03-21
- 修回日期: 2022-05-10
- 刊出日期: 2024-01-19

Infrared and Visible Image Fusion Based on NSCT Combined with Saliency Map and Region Energy

School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China

摘要

摘要: 针对传统的红外与可见光图像融合出现的清晰度和对比度偏低，目标不够突出的问题，本文提出了一种基于Non-subsampled Contourlet（NSCT）变换结合显著图与区域能量的融合方法。首先，使用改进的频率调谐（Frequency-tuned, FT）方法求出红外图像显著图并归一化得到显著图权重，单尺度Retinex（Single-scale Retinex, SSR）处理可见光图像。其次，使用NSCT分解红外与可见光图像，并基于归一化显著图与区域能量设计新的融和权重来指导低频系数融合，解决了区域能量自适应加权容易引入噪声的问题；采用改进的“加权拉普拉斯能量和”指导高频系数融合。最后，通过逆NSCT变换求出融合图像。本文方法与7种经典方法在6组图像中进行对比实验，在信息熵、互信息、平均梯度和标准差指标中最优，在空间频率中第一组图像为次优，其余图像均为最优结果。融合图像信息量丰富、清晰度高、对比度高并且亮度适中易于人眼观察，验证了本文方法的有效性。
- 图像融合 /
- Non-subsampled Contourlet变换 /
- 区域能量自适应加权 /
- 拉普拉斯能量和
Abstract: To address the problems of low clarity and contrast of indistinct targets in traditional infrared and visible image-fusion algorithms, this study proposes a fusion method based on non-subsampled contourlet transform (NSCT) combined with a saliency map and region energy. First, an improved frequency-tuning (FT) method is used to obtain the infrared image saliency map, which is subsequently normalized to obtain the saliency map weight. A single-scale retinex (SSR) algorithm is then used to enhance the visible image. Second, NSCT is used to decompose the infrared and visible images, and a new fusion weight is designed based on the normalized saliency map and region energy to guide low-frequency coefficient fusion. This solves the problem of region-energy adaptive weighting being prone to introducing noise, and the improved "weighted Laplace energy sum" is used to guide the fusion of high-frequency coefficients. Finally, the fused image is obtained by inverse NSCT. Six groups of images were used to compare the proposed method with seven classical methods. The proposed method outperformed others in terms of information entropy, mutual information, average gradient, and standard deviation. Regarding spatial frequency, the first group of images was second best, and the remaining images exhibited the best results. The fusion images displayed rich information, high resolution, high contrast, and moderate brightness, demonstrating suitability for human observation, which verifies the effectiveness of this method.
- image fusion /
- Non-subsampled Contourlet Transform /
- adaptive weight of regional energy /
- sum modified Laplacian

HTML全文

图 1 红外图像显著图

Figure 1. Infrared image saliency map

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图 2 可见光图像的增强

Figure 2. Enhancement of visible light images

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图 3 NSCT分解示意图

Figure 3. Schematic diagram of NSCT decomposition

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图 4 本文图像融合方法流程框图

Figure 4. Flow chart of the image fusion method in this paper

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图 5 Nato_camp的融合结果

Figure 5. Fusion results of Nato_camp

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图 6 Tree的融合结果

Figure 6. Fusion results of Tree

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图 7 Duine的融合结果

Figure 7. Fusion results of Duine

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图 8 APC_4的融合结果

Figure 8. Fusion results of APC_4

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图 9 Kaptein_1654的融合结果

Figure 9. Fusion results of Kaptein_1654

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图 10 Movie_18的融合结果

Figure 10. Fusion results of Movie_18

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表 1 Nato_camp客观评价结果

Table 1 Objective evaluation results of Nato_camp

Methods	IE	SF	MI	AG	SD
LP	6.6022	13.9059	1.8726	5.6680	28.3882
RP	6.5073	14.7712	1.7414	5.8658	27.3435
DTCWT	6.4292	12.8851	1.8237	5.1054	25.6576
CVT	6.3256	11.8559	1.7603	4.7871	24.0154
NSCT	6.6369	13.5828	1.8622	5.6284	28.6006
DCHWT	6.3231	11.0041	1.7605	4.4255	25.8427
Hybrid_MSD	6.7020	15.1560	2.0825	6.0049	29.1284
Proposed	7.0235	14.8216	2.6280	6.2154	37.1931

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表 2 Tree评价结果

Table 2 Objective evaluation results of Tree

Methods	IE	SF	MI	AG	SD
LP	5.8484	7.7347	1.6911	3.0154	14.7225
RP	5.8788	9.0936	1.5948	3.2999	15.1486
DTCWT	5.7484	7.1703	1.6836	2.7127	13.5645
CVT	5.7172	6.8121	1.6988	2.5939	13.0307
NSCT	5.8498	7.4456	1.6643	2.9577	14.6721
DCHWT	6.0325	6.1470	1.5695	2.4196	16.3405
Hybrid_MSD	6.2954	8.5220	1.8939	3.2627	20.2859
Proposed	6.8678	11.3701	3.1874	4.8995	31.3029

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表 3 Duine客观评价结果

Table 3 Objective evaluation results of Duine

Methods	IE	SF	MI	AG	SD
LP	5.9780	7.4565	1.5175	3.3497	15.4549
RP	5.8923	6.9660	1.5174	3.0050	14.5825
DTCWT	5.8808	6.7566	1.5059	3.0241	14.4632
CVT	5.8322	6.5233	1.4829	2.9103	14.0198
NSCT	5.9806	7.0812	1.5181	3.2809	15.5069
DCHWT	5.8015	5.7144	1.5240	2.5810	13.6918
Hybrid_MSD	5.9472	8.2933	1.5553	3.6047	15.2660
Proposed	7.1126	13.2301	3.1155	6.1093	35.0350

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表 4 APC_4客观评价结果

Table 4 Objective evaluation results of APC_4

Methods	IE	SF	MI	AG	SD
LP	5.8574	12.7384	0.9065	5.5624	14.5848
RP	5.6555	12.4937	0.7502	5.0838	12.9853
DTCWT	5.6766	11.7014	0.8259	5.0618	12.8683
CVT	5.5498	11.3298	0.7902	4.8656	11.6973
NSCT	5.8743	12.1900	0.8733	5.4444	14.7461
DCHWT	5.5262	9.7761	0.8671	4.2252	11.4534
Hybrid_MSD	5.9514	14.0328	1.0230	5.9508	15.5852
Proposed	6.6951	16.5562	2.8247	7.5079	25.6268

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表 5 Kaptein_1654客观评价结果

Table 5 Objective evaluation results of Kaptein_1654

Methods	IE	SF	MI	AG	SD
LP	6.6557	19.0519	2.2753	6.8009	36.8878
RP	6.7122	19.7509	2.2324	6.9286	34.3867
DTCWT	6.4858	17.9294	2.2036	6.3067	31.2040
CVT	6.3880	16.3414	2.2711	5.6601	28.7014
NSCT	6.6451	18.7667	2.2189	6.7824	35.7430
DCHWT	6.7437	15.4424	2.2980	5.5617	36.3196
Hybrid_MSD	6.8692	20.4999	2.1659	7.1597	37.6306
Proposed	7.0479	20.6661	3.0611	7.7455	53.3902

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表 6 Movie_18客观评价结果

Table 6 Objective evaluation results of Movie_18

Methods	IE	SF	MI	AG	SD
LP	5.9545	8.3646	1.7206	3.0967	17.9892
RP	5.7520	9.0084	1.5003	3.1423	16.3032
DTCWT	5.6640	7.8037	1.6294	2.8594	14.5863
CVT	5.5991	7.6734	1.6439	2.8605	13.9172
NSCT	5.8977	8.2244	1.6706	3.0956	17.1809
DCHWT	5.5755	6.6402	1.6596	2.4455	15.0237
Hybrid_MSD	6.2333	9.2791	2.3197	3.3676	21.1429
Proposed	6.8108	10.0577	3.3102	3.7343	47.7950

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施引文献(10)

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