{"title":"Maximum Local Energy Based Multifocus Image Fusion in Mirror Extended Curvelet Transform Domain","authors":"Lifeng Zhang, Huimin Lu, Yujie Li, S. Serikawa","doi":"10.1109/SNPD.2012.16","DOIUrl":null,"url":null,"abstract":"In this paper, we firstly propose the maximum local energy (MLE) method to calculate the low frequency coefficients of images and compare the results with those of mirror extended curve let transform, which enhance the edge features and details of images. An image fusion step was performed as follows: First, we obtained the coefficients of two different types of images through mirror extended curve let transform. Second, we selected the low frequency coefficients by maximum local energy and obtaining the high-frequency coefficients using the absolute maximum value (AMV) method. Finally, the fused image was obtained by performing an inverse mirror extended curve let transform. In addition to human vision analysis, the images were also compared through quantitative analysis. multifocus images were used in the experiments to compare the results among the beyond wavelets. The numerical experiments reveal that maximum local energy is a new strategy for attaining image fusion with satisfactory performance.","PeriodicalId":387936,"journal":{"name":"2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD.2012.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we firstly propose the maximum local energy (MLE) method to calculate the low frequency coefficients of images and compare the results with those of mirror extended curve let transform, which enhance the edge features and details of images. An image fusion step was performed as follows: First, we obtained the coefficients of two different types of images through mirror extended curve let transform. Second, we selected the low frequency coefficients by maximum local energy and obtaining the high-frequency coefficients using the absolute maximum value (AMV) method. Finally, the fused image was obtained by performing an inverse mirror extended curve let transform. In addition to human vision analysis, the images were also compared through quantitative analysis. multifocus images were used in the experiments to compare the results among the beyond wavelets. The numerical experiments reveal that maximum local energy is a new strategy for attaining image fusion with satisfactory performance.
本文首先提出了最大局部能量法(maximum local energy, MLE)来计算图像的低频系数,并与镜面扩展曲线let变换的结果进行了比较,增强了图像的边缘特征和细节。图像融合步骤如下:首先,通过镜像扩展曲线let变换得到两种不同类型图像的系数;其次,利用最大局部能量选择低频系数,利用绝对最大值(AMV)法获得高频系数;最后,通过逆镜像扩展曲线let变换得到融合图像。除人眼视觉分析外,还通过定量分析对图像进行比较。实验中采用多聚焦图像,比较了不同小波之间的结果。数值实验表明,局部能量最大化是一种获得满意图像融合效果的新策略。