[1]任晓霞,孙秀明,耿鹏,等.多小波和NSDFB组合域递归滤波多聚焦图像融合[J].智能系统学报编辑部,2016,11(2):241-248.[doi:10.11992/tis.201509017]
REN Xiaoxia,SUN Xiuming,GENG Peng,et al.Multifocus image fusion using a recursive filter in the combined domain of multiwavelets and NSDFB[J].CAAI Transactions on Intelligent Systems,2016,11(2):241-248.[doi:10.11992/tis.201509017]
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REN Xiaoxia,SUN Xiuming,GENG Peng,et al.Multifocus image fusion using a recursive filter in the combined domain of multiwavelets and NSDFB[J].CAAI Transactions on Intelligent Systems,2016,11(2):241-248.[doi:10.11992/tis.201509017]
多小波和NSDFB组合域递归滤波多聚焦图像融合
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第2期
页码:
241-248
栏目:
学术论文—智能系统
出版日期:
2016-04-25
- Title:
- Multifocus image fusion using a recursive filter in the combined domain of multiwavelets and NSDFB
- Keywords:
- image processing; image fusion; recursive filter; modified spatial frequency; multiwavelett
- 分类号:
- TP391
- 摘要:
- 多小波能同时满足正交、紧支、对称等对信号处理十分重要的特性,结合多小波变换的多尺度特点和非子采样方向滤波器组变换的多方向性,提出了一种新的基于多小波和非子采样方向滤波器组的多尺度多方向变换。对于高频系数首先计算其修改空间频率,然后利用域变换递归滤波进行滤波的融合规则;低频系数采用了修改拉普拉斯能量和的(SML)融合规则。通过与其他融合方法进行实验对比,实验结果表明:本文提出的融合方法能够更加有效地选择源图像中的聚焦良好区域,并且引入的伪影信息较少;此外,与其他融合方法相比本文方法的客观评价结果也是最好的。
- Abstract:
- The multiwavelet transform has properties of orthogonality, tight frame, and symmetry, which are vital to signal processing. In this study, a new transform, called as MNSDFB, is proposed by combining the multi-wavelet and nonsubsampled directional filter banks. The domain transform recursive filter is adopted to fuse the filters after the spatial frequency of the high frequency coefficient is calculated. The modified sum-modified-Laplacian (SML) is employed in the low pass subbands as a focus measurement to select fused coefficients. The presented fusion rule in the high pass subband can distinguish the focused regions from the blurred regions. The proposed fusion method was compared with three other fusion methods. The experimental results demonstrate that the proposed fusion method can select the focused regions while introducing few artifacts into the final merged image. Furthermore, its objective criteria, such as MI and QAB/F, are better than those of the other three methods.
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备注/Memo
收稿日期:2015-9-9;改回日期:。
基金项目:河北省自然科学基金项目(F2013210094,F2013210109).
作者简介:任晓霞,女,1982年生,讲师,主要研究方向为计算机技术、图形图像技术;孙秀明,男,1978年生,副教授,主要研究方向为图像去噪、图像融合以及为数字化资源开发;耿鹏,男,1979年生,副教授,主要研究方向为图像融合、小波分析。主持河北省自然科学基金项目、河北省教育厅高等学校科学研究项目等多项,发表学术论文多篇,其中被SCI检索4篇,被EI检索6篇。
通讯作者:耿鹏.E-mail:Gengpeng@stdu.edu.cn.
更新日期/Last Update:
1900-01-01