Multi-Focus Image Fusion Using Curvelet Transform

Yong Li

doi:10.4028/www.scientific.net/AMM.347-350.2743

Paper Titles

An Improved Algorithm Based on LEACH Protocol
p.2725

Compute Three-Dimensional Reconstruction of Brachial Plexus and its Surrounding Tissues by Using Chinese Visible Human (CVH) Data Anatomical Foundation for Brachial Plexus Entrapment
p.2728

The Design of Social Security Information Management System
p.2734

Application of Multimedia and Network English Teaching in Multiple Intelligence Development
p.2739

Multi-Focus Image Fusion Using Curvelet Transform
p.2743

Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation
p.2747

Edge-Magic Total Labellings of some Network Models
p.2752

A Method of Opinion Tendency Analyzing Based on Regular Expression
p.2758

Research on Iterative Method in Solving Linear Equations on the Hadoop Platform
p.2763

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350Multi-Focus Image Fusion Using Curvelet Transform

Multi-Focus Image Fusion Using Curvelet Transform

Abstract:

Curvelet transform is a new muiti-scale geometic analysis, which has the characteristics of anisotropy. It is more suitable for the analysis of image curve edge characteristics than wavelet. Thus, in this paper it is applied to multi-focus image fusion, and used fusion rules that suitable for the characteristics of multi-focus image fusion.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

2743-2746

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.2743

Citation:

Cite this paper

Online since:

August 2013

Authors:

Yong Li

Keywords:

Component, Curvelet Transform Style, Muiti-Scale Geometic Analysis, Multi-Focus Image

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Ranchin T, Wald L. The wavelet transform for the analysis of remotely sensed images[J]. International Journal of Remote Sensing, 1993, 14(3): 615-619.

DOI: 10.1080/01431169308904362

Google Scholar

[2] E. J. Candès, Harmonic analysis of neural networks, Appl. Comput. Harmon. Anal., vol. 6, p.197–218, (1999).

Google Scholar

[3] J. L. Starck, E. J. Candès, and D. L. Donoho, The curvelet transform for image denosing, IEEE Trans. Image Process., vol. 11, no. 6, p.670–684, Jun. (2002).

DOI: 10.1109/tip.2002.1014998

Google Scholar

[4] Donoho D.L. Orthonormal ridgelets and linear singularities [R]. Tech. Report, Department of Statistics, Stanford University, (1998).

Google Scholar

[5] Candès E J, Demanet L, Donoho D L, et al. Fast discrete curvelet transforms. Appied and Computational Mathe ematics, Califormia Institute of Technology, 2005: 1-43.

Google Scholar

[6] Qang Zhan xia, et al. Remote sensing image fusion based on wavelet transform local variance [J]. Huazhong University of Science and Technology (Natural Science), 2003, 31(6): 89-91.

Google Scholar

[7] L. Alparone, S. Baronti, A. Garzelli, F. Nencini. Remote sensing image fusion using the curvelet transforms. International Journal on Information Fusion, Elsevier, 2007, 8(2): 143-15.

DOI: 10.1016/j.inffus.2006.02.001

Google Scholar

[8] Xiao Gang, Jing Zhong liang, Wu Jian min, Liu Congyi. Synthetically evaluation system for multi-source image fusion and experiment analysis[J]. Journal of Shanghai Jiaotong University, 2006, 11(3): 263-270.

Google Scholar