M-SWF域红外与可见光图像结构相似性融合OA北大核心CSTPCD
Structural Similarity Fusion of Infrared and Visible Image in the M-SWF Domain
为了解决常规滤波器组在红外与可见光图像融合领域中存在提取结构信息不充分和融合视觉效果不佳的问题,本文提出了一种基于多尺度滑动窗口滤波器(Multi-scale Sliding Window Filter,M-SWF)图像融合方法.首先,提出一种基于 SWF 的多尺度图像分解方法实现对源图像的结构细节层和基础层提取;其次,采用L1范数融合规则(L1-Fusion,L1F)整合结构细节层,提取图像的结构信息;然后,利用一种图像能量贡献融合规则(Energy Attribute-Fusion,EAF)整合基础层,突出显著性目标;最后,融合图像通过叠加整合后的多尺度结构细节层和基础层得到.实验首先通过分析能量贡献系数,从主客观方面得到M-SWF域内红外与可见光图像融合较为适宜的能量贡献系数;其次,在该取值下,本文提出的M-SWF融合模型与其他的融合方法相比,不仅提高了对源图像结构信息的提取能力,而且通过整合图像的能量属性,改善了融合效果不佳问题,有效地突出了显著性目标.
This study introduces a multiscale sliding window filter(M-SWF)image fusion method to address issues with traditional filter banks in infrared and visible image fusion.First,a multiscale image decomposition method based on SWF is proposed to extract the structural detail layers and base layers of the source image.Second,the L1 norm fusion rule(L1-Fusion,L1F)is used to integrate the structural detail layers,which can extract the structure of the image.Then,to highlight the salient objects,energy attribute fusion(EAF),which is a rule for fusing image energy contributions,is used to integrate the base layers,and the fusion results are obtained by stacking the integrated multiscale structure detail layers and base layers.The energy contribution coefficient was analyzed,and a suitable energy contribution coefficient was obtained for the fusion of infrared and visible images in the M-SWF domain from subjective and objective perspectives.Compared with other fusion methods,the M-SWF not only improves the ability to extract the structural information of the source image but also improves the poor fusion effect and effectively highlights salient targets by integrating the energy attributes of the image.
李威;田时舜;刘广丽;邹文斌
深圳大学 电子与信息工程学院,广东 深圳 518060
计算机与自动化
红外图像可见光图像图像融合滑动窗口滤波器结构相似性
infrared imagevisible imageimage fusionside window filterstructural similarity
《红外技术》 2024 (003)
280-287 / 8
国家自然科学基金项目(62171294,62101344),广东省自然科学基金(2022A1515010159),深圳自然科学基金(JCYJ20200109105832261,JCYJ20190808122409660),深圳市科技计划重点项目(20220810180617001).
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