一种基于蚁狮最大熵算法与引导滤波的图像融合算法

蒋杰伟; 刘尚辉; 金库; 魏戌盟; 巩稼民

doi:10.11999/JEIT221499

一种基于蚁狮最大熵算法与引导滤波的图像融合算法

doi: 10.11999/JEIT221499

1.
西安邮电大学电子工程学院西安 710121
2.
西安邮电大学通信与信息工程学院西安 710121

基金项目: 国家自然科学基金(61775180, 62276210)，陕西省自然科学基础研究计划(2022JM-380)

详细信息

作者简介:
蒋杰伟：男，博士，讲师，研究方向为人工智能、图像处理等

刘尚辉：男，硕士生，研究方向为图像处理、图像融合等

金库：男，硕士生，研究方向为光通信与光信息技术

魏戌盟：女，硕士生，研究方向为光通信与光信息技术

巩稼民：男，博士，教授，研究方向为光通信、光电子技术

通讯作者:
刘尚辉　lsh81687039@163.com

中图分类号: TN911.73; TN713
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- 被引次数: 0
出版历程
- 收稿日期: 2022-12-02
- 修回日期: 2022-12-23
- 网络出版日期: 2022-12-28
- 刊出日期: 2023-04-10

An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering

1.
School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (61775180, 62276210), The Natural Science Basic Research Program of Shaanxi (2022JM-380)

摘要

摘要:
传统红外与可见光图像融合算法中易出现目标提取不够充分、细节丢失等问题，导致融合效果不理想，从而无法应用于目标检测、跟踪或识别等领域。因此，该文提出一种基于蚁狮优化算法(ALO)改进的最大香农(Shannon)熵分割法结合引导滤波的红外与可见光图像融合方法。首先，使用蚁狮最大熵分割法(ALO-MES)对红外图像进行目标提取，然后，对红外和可见光图像使用非下采样剪切波变换(NSST)，并对获得的低频和高频分量进行引导滤波。由提取的目标图像与增强后的红外和可见光低频分量通过低频融合规则得到低频融合系数，增强后的高频分量通过双通道脉冲发放皮层模型(DCSCM)得到高频融合系数，最后经NSST逆变换得到融合图像。实验结果表明，所提算法能够得到目标明确、背景信息清晰的融合图像。
- 图像融合 /
- 蚁狮优化算法 /
- 最大Shannon熵分割 /
- 引导滤波 /
- 双通道脉冲发放皮层模型
Abstract:
Traditional fusion algorithms of infrared and visible images often have defects such as insufficient target extraction and loss of details, which lead to unsatisfactory fusion effects, and the fused image can not be applied to target detection, tracking or recognition. Therefore, a fusion method of infrared and visible images based on guided filtering and improved maximum Shannon entropy segmentation method using Ant Lion Optimization algorithm (ALO) is proposed. First, Ant Lion Optimized Maximum Entropy Segmentation (ALO-MES) algorithm is used to extract the target from infrared image. Then, the Non-Subsampled Shearlet Transform (NSST) is performed on the infrared and visible images to obtained the low frequency and high frequency sub-bands, and conduct guided filtering for obtained sub-bands. The low-frequency fusion coefficient is obtained from the extracted target image and the enhanced infrared and visible low-frequency components through the fusion rule based on ALO-MES. And the high-frequency fusion coefficient is obtained by the enhanced high-frequency sub-bands components through Dual-Channel Spiking Cortical Model (DCSCM). Finally, the fusion image is obtained by inverse NSST transform. The experimental results show that the proposed algorithm can get fusion image with clear target and background information.
- Image fusion /
- Ant Lion Optimization (ALO) algorithm /
- Maximum Shannon entropy segmentation /
- Guided filtering /
- Dual Channel Spiking Cortical Model (DCSCM)

HTML全文

图 1 本文融合方法流程图

下载: 全尺寸图片幻灯片

图 2 3种不同图像分割方法效果对比

下载: 全尺寸图片幻灯片

图 3 5组可见光与红外光图像

下载: 全尺寸图片幻灯片

图 4 不同算法融合结果对比

下载: 全尺寸图片幻灯片

表 1 4种图像分割算法对比

算法	Iter(1)/ Th(1)	Iter(2)/ Th(2)	Iter(3)/ Th(3)	AV-Iter	AV-Time(ms)
MES	1/155	1/155	1/155	1.0	134
PSO-MES	2/154	2/157	2/168	2.0	93
FA-MES	2/154	3/155	3/155	2.6	109
ALO-MES	2/155	1/155	2/155	1.6	43

下载: 导出CSV

表 2 5组融合图像客观评价指标

实验图像组	算法	AG	STD	MI	EN	Q^AB/F	SSIM
man	MGFF	5.7765	32.0072	1.5041	6.8010	0.4201	0.5480
	RGFFT	5.6651	39.3142	3.0086	7.1654	0.4781	0.5105
	MST	5.5076	33.1331	2.8316	6.6562	0.4402	0.5192
	SCMGF	5.4963	39.4170	3.4252	7.1643	0.4772	0.5186
	本文	5.7943	39.5754	2.6810	7.1687	0.4336	0.5314
meeting	MGFF	5.6886	31.5410	1.4270	6.8607	0.4759	0.1299
	RGFFT	5.2517	45.9761	2.3598	6.9519	0.4546	0.4904
	MST	5.3389	54.6009	4.2285	6.7975	0.5562	0.4873
	SCMGF	5.6384	36.4719	1.8304	7.1768	0.4375	0.4795
	本文	5.4586	46.1790	2.4660	6.9586	0.4770	0.4978
traffic	MGFF	4.4707	31.8944	1.8872	6.5396	0.5653	0.4394
	RGFFT	4.5027	35.8259	4.3799	6.7687	0.6721	0.4747
	MST	4.1413	29.7569	2.6295	6.2525	0.5575	0.4219
	SCMGF	4.2004	35.1244	2.7851	6.7725	0.5175	0.4054
	本文	4.5927	37.5240	3.4377	6.7793	0.6081	0.4712
tree	MGFF	7.7364	26.4433	0.9584	0.9584	0.3706	0.5471
	RGFFT	7.0601	40.4418	4.1932	7.1627	0.5510	0.5181
	MST	7.2675	35.5588	2.6991	6.7720	0.4449	0.5409
	SCMGF	6.8891	40.1562	3.2675	7.1576	0.4772	0.5272
	本文	7.4905	40.4902	2.8634	7.1664	0.4279	0.5537
Kaptein	MGFF	6.0267	35.4698	1.5254	6.6835	0.4707	0.5218
	RGFFT	5.9171	56.9160	3.4177	7.2780	0.5115	0.4509
	MST	5.8412	47.7283	3.2752	6.6078	0.5063	0.4955
	SCMGF	5.7244	58.6063	4.5781	7.2761	0.5273	0.4796
	本文	6.1993	61.2249	3.4664	7.3985	0.4888	0.4888

下载: 导出CSV

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