Article

Multifocus image fusion using spatial features and support vector machine

Authors:

Shutao Li,

Yaonan WangAuthors Info & Claims

ISNN'05: Proceedings of the Second international conference on Advances in neural networks - Volume Part II

Pages 753 - 758

Published: 30 May 2005 Publication History

Abstract

This paper describes an application of support vector machine to pixel-level multifocus image fusion problem based on the use of spatial features of image blocks. The algorithm first decomposes the source images into blocks. Given two of these blocks (one from each source image), a SVM is trained to determine which one is clearer. Fusion then proceeds by selecting the clearer block in constructing the final image. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach, particularly when there is movement in the objects or misegistration of the source images.

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Published In

ISNN'05: Proceedings of the Second international conference on Advances in neural networks - Volume Part II

May 2005

934 pages

ISBN:3540259139

Editors:
Jun Wang
The Chinese University of Hong Kong, Department of Automation and Computer-Aided Engineering, New Territories, Hong Kong
,
Xiaofeng Liao
Chongqing University, School of Computer Science and Engineering, Chongqing, China
,
Zhang Yi
University of Electronic Science and Technology of China, School of Computer Science and Engineering, Sichuan, China

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 30 May 2005

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Multifocus image fusion using region segmentation and spatial frequency

Multifocus Image Fusion Using Wavelet-Domain-Based Deep CNN

Spiking cortical model for multifocus image fusion

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