摘要
目的针对图像融合中信息量不够丰富和边缘细节模糊问题,结合多尺度分析、稀疏表示和显著性特征等图像表示方法,提出一种卷积稀疏与细节显著图解析的图像融合方法。方法首先构造一种自适应样本集,训练出契合度更高的字典滤波器组。然后将待融合图像进行多尺度分析得到高低频子图,对低频子图进行卷积稀疏表示,通过权重分析构建一种加权融合规则,得到信息量更加丰富的低频子图;对高频子图构造细节显著图,进行相似性分析,建立一种高频融合规则,得到边缘细节更加凸显的高频子图。最后进行相应逆变换得到最终图像。结果实验在随机挑选的3组灰度图像集和4组彩色图像集上进行,与具有代表性的7种融合方法进行效果对比。结果表明,本文方法的视觉效果明显较优,平均梯度上依次平均提高39.3%、32.1%、34.7%、28.3%、35.8%、28%、30.4%;在信息熵上依次平均提高6.2%、4.5%、1.9%、0.4%、1.5%、2.4%、2.9%;在空间频率上依次平均提高31.8%、25.8%、29.7%、22.2%、28.6%、22.9%、25.3%;在边缘强度上依次平均提高39.5%、32.1%、35.1%、28.8%、36.6%、28.7%、31.3%。结论本文方法在一定程度上解决了信息量不足的问题,较好地解决了图像边缘细节模糊的问题,使图像中奇异性更加明显的内容被保留下来。
Objective Image fusion is the process of using multiple image information of the same scene according to certain rules to obtain better fusion image.The fusion image contains the outstanding features of the image to be fused,which can improve the utilization of image information and provide more accurate help for later decision-making based on the image.Multi-scale analysis method,sparse representation method,and saliency method are three kinds of image representation methods that can be used in image fusion.Multi-scale analysis method is an active field in image fusion,but only the appropriate transformation method can improve the performance of the fusion image.The sparse representation method has good performance for image representation,but multi-value representation of the image easily leads to the loss of details.The significance method is unique due to its ability to capture the outstanding target in the image.However,visual saliency is a subjective image description index,and the proper construction of saliency map is an urgent problem to be solved.To address the problem of insufficient information and fuzzy edge details in image fusion,an image fusion method of convolution sparsity and detail saliency map analysis is proposed,which combines the advantages of multi-scale analysis,sparse representation method,and saliency method and at the same time avoids their disadvantages as much as possible.Method First,to address the insufficient image information after fusion,a multi-directional method is proposed to construct the adaptive training sample set.Then through dictionary training,a more abundant dictionary filter bank suitable for the image to be fused is obtained.Second,low-and high-frequency subgraphs are obtained by multi-scale analysis.The low-frequency subgraph contains a lot of basic information of source image,and it is represented by convolution sparsity using the trained adaptive dictionary filter bank.In this way,the sparse matrix of global single-value representation is obtained.The activity o
作者
杨培
高雷阜
訾玲玲
Yang Pei;Gao Leifu;Zi Lingling(Institute for Optimization and Decision Analytics,Liaoning Technical University,Fuxin 123000,China;College of Electronic and Information Engineering,Liaoning Technical University,Huludao 125105,China)
出处
《中国图象图形学报》
CSCD
北大核心
2021年第10期2433-2449,共17页
Journal of Image and Graphics
基金
国家自然科学基金项目(61702241)
辽宁省自然科学基金项目(2019-ZD-0041,2020-MS-301)
辽宁省教育厅重点攻关项目(LJ2019ZL001,LJ2020ZD002)。
关键词
多尺度分析
自适应样本集
卷积稀疏
细节显著图
图像融合
multi-scale analysis
adaptive sample set
convolution sparsity
detail saliency map
image fusion
