基于ResNet-50和Laplacian滤波的红外可见光融合方法研究

doi:10.16182/j.issn1004731x.joss.25-FZ0696

摘要/Abstract

摘要：

为解决现有的红外与可见光图像融合技术通常因对比度不足、光谱畸变，以及计算复杂度高而产生伪影的问题，提出了一种基于ResNet-50和Laplacian滤波的融合框架。利用ResNet-50网络提取浅层和深层特征，并进行多尺度特征融合；通过Laplacian滤波优化特征信息，引入自动判别器进一步提升融合效果。仿真结果表明：和典型算法相比，该方法在TNO和VIFB数据集上的信息熵平均提升2.71%和2.16%，视觉保真度平均提升5.98%和7.86%，小波特征互信息平均提升12.57%和14.63%，证明了其在提升图像融合质量方面的有效性。

关键词: 图像融合, ResNet-50, Laplacian滤波, 深度学习, 自动判别器

Abstract:

In order to solve the problem that existing infrared and visible light image fusion techniques often suffer from artifacts caused by insufficient contrast, spectral distortion, and high computational complexity, a fusion framework based on ResNet-50 and Laplacian filtering was proposed. ResNet-50 was used to extract shallow and deep features, followed by multi-scale feature fusion. Laplacian filtering was applied to optimize feature information, and an automatic discriminator was introduced to further improve the fusion effect. Simulation results show that, compared with comparison algorithms, the proposed method achieves an average increase of 2.71% and 2.16% in information entropy, 5.98% and 7.86% in visual information fidelity, and 12.57% and 14.63% in wavelet-based feature mutual information on the TNO and VIFB datasets, respectively, which proves its effectiveness in improving image fusion quality.

Key words: image fusion, ResNet-50, Laplacian filtering, deep learning, automatic discriminator

中图分类号:

TP571

汪潇,李向阳,梁丰等 . 基于ResNet-50和Laplacian滤波的红外可见光融合方法研究[J]. 系统仿真学报, 2025, 37(12): 3202-3211.

Wang Xiao,Li Xiangyang,Liang Feng,et al . Research on Infrared and Visible Light Fusion Method Based on ResNet-50 and Laplacian Filtering[J]. Journal of System Simulation, 2025, 37(12): 3202-3211.

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是否采用初始权重图		信息熵	标准差	空间频率	小波特征互信息	视觉保真度	平均梯度
TNO	否	6.632 7	7.356 6	0.047 5	0.456 2	0.674 3	5.867 1
	是	6.727 4	8.473 5	0.052 3	0.502 9	0.702 8	6.173 9
	提升率/%	1.43	15.18	10.11	10.24	4.23	5.23
VIFB	否	6.925 3	7.537 1	0.046 4	0.453 4	0.685 3	5.429 4
	是	7.129 5	8.068 2	0.049 1	0.500 2	0.770 4	6.058 6
	提升率/%	2.95	7.05	5.82	10.32	12.42	11.59

有无采用Laplacian		信息熵	标准差	空间频率	小波特征互信息	视觉保真度	平均梯度
TNO	无	6.623 5	7.958 9	0.042 3	0.427 6	0.651 3	6.271 7
	有	6.913 4	8.214 4	0.063 4	0.573 3	0.758 3	6.578 9
	提升率/%	4.38	3.21	49.88	34.07	16.43	4.90
VIFB	无	6.237 5	9.221 1	0.029 6	0.461 8	0.550 7	5.006 6
	有	6.830 5	9.968 8	0.042 0	0.565 7	0.659 1	6.801 0
	提升率/%	9.51	8.11	41.89	22.50	19.68	35.84

有无自动判别器		信息熵	标准差	空间频率	小波特征互信息	视觉保真度	平均梯度
TNO	无	7.144 8	8.588 4	0.056 3	0.396 0	0.659 1	6.471 7
	有	8.917 6	9.968 8	0.072 0	0.591 4	0.655 7	6.809 0
	提升率/%	24.81	16.07	27.89	49.34	-0.52	5.21
VIFB	无	6.485 3	8.688 3	0.048 7	0.434 4	0.625 5	6.122 7
	有	7.186 3	9.805 6	0.094 1	0.459 5	1.088 6	7.191 4
	提升率/%	10.81	12.86	93.22	5.78	74.04	17.45

实验	方法	信息熵	标准差	空间频率	小波特征互信息	视觉保真度	平均梯度
第 1 组	CBF	5.905 1	8.482 3	0.030 0	0.387 3	0.607 7	3.175 2
	JSR	6.235 0	8.844 6	0.043 2	0.398 9	0.722 1	4.239 3
	GTF	7.235 3	8.747 8	0.033 5	0.428 7	0.753 3	4.522 1
	ConvSR	7.832 6	9.724 7	0.044 8	0.572 1	0.782 2	4.964 7
	DeepFuse	8.200 3	10.442 9	0.063 3	0.638 8	0.843 9	5.338 9
	Ours	8.422 6	9.852 6	0.088 2	0.718 3	0.894 4	6.329 5
第 2 组	CBF	6.167 5	8.366 2	0.057 7	0.345 3	0.623 5	3.355 2
	JSR	6.449 8	8.275 3	0.046 6	0.396 5	0.762 9	4.498 2
	GTF	7.238 7	8.028 3	0.059 2	0.387 2	0.773 4	4.298 5
	ConvSR	8.092 8	9.337 4	0.062 2	0.446 3	0.788 4	5.229 5
	DeepFuse	9.287 3	11.128 5	0.075 3	0.652 8	0.829 4	6.338 9
	Ours	9.488 2	11.448 2	0.062 9	0.748 3	0.894 6	7.025 3

实验	信息熵	标准差	空间频率	小波特征互信息	视觉保真度	平均梯度
平均提升	+2.44	-1.39	+22.87	+13.54	+6.92	+14.69
第1组	+2.71	-5.65	+39.34	+12.57	+5.98	+18.55
第2组	+2.16	+2.87	-16.47	+14.63	+7.86	+10.83