Feature-level image fusion technique based on wavelet transform

Zhigang Fan; Songling Fu; Runshun Li; Baojun Zuo

doi:10.1117/12.470952

Feature-level image fusion technique based on wavelet transform

Zhigang Fan^*
, Songling Fu
, Runshun Li
, Baojun Zuo

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

The character of MRA (Multi-resolution Analysis) of wavelet transform can make the image be divided into different frequency spaces, so the image information in different frequency spaces can be processed and the features of the original image can be controlled. Wavelet transform is used widely in image compressing, edge detecting, noise filtering, image fusing etc. Based on Mallat fast algorithm in this paper, the noise of the image is filtered with threshold method after decomposing. Then the weighing operator and the comparing operator are applied for the image reconstructing, and feature-level image fusing is accomplished in practice. In contrast, it is better than the effect of other methods.

Original language	English
Pages (from-to)	289-292
Number of pages	4
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	4919
DOIs	https://doi.org/10.1117/12.470952
State	Published - 2002
Event	Advanced Materials and Devices for Sensing and Imaging - Shanghai, China Duration: 17 Oct 2002 → 18 Oct 2002

Keywords

Image fusion
MRA (multi-resolution analysis)
Wavelet transform

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10.1117/12.470952

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title = "Feature-level image fusion technique based on wavelet transform",

abstract = "The character of MRA (Multi-resolution Analysis) of wavelet transform can make the image be divided into different frequency spaces, so the image information in different frequency spaces can be processed and the features of the original image can be controlled. Wavelet transform is used widely in image compressing, edge detecting, noise filtering, image fusing etc. Based on Mallat fast algorithm in this paper, the noise of the image is filtered with threshold method after decomposing. Then the weighing operator and the comparing operator are applied for the image reconstructing, and feature-level image fusing is accomplished in practice. In contrast, it is better than the effect of other methods.",

keywords = "Image fusion, MRA (multi-resolution analysis), Wavelet transform",

author = "Zhigang Fan and Songling Fu and Runshun Li and Baojun Zuo",

year = "2002",

doi = "10.1117/12.470952",

language = "英语",

volume = "4919",

pages = "289--292",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

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note = "Advanced Materials and Devices for Sensing and Imaging ; Conference date: 17-10-2002 Through 18-10-2002",

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TY - JOUR

T1 - Feature-level image fusion technique based on wavelet transform

AU - Fan, Zhigang

AU - Fu, Songling

AU - Li, Runshun

AU - Zuo, Baojun

PY - 2002

Y1 - 2002

N2 - The character of MRA (Multi-resolution Analysis) of wavelet transform can make the image be divided into different frequency spaces, so the image information in different frequency spaces can be processed and the features of the original image can be controlled. Wavelet transform is used widely in image compressing, edge detecting, noise filtering, image fusing etc. Based on Mallat fast algorithm in this paper, the noise of the image is filtered with threshold method after decomposing. Then the weighing operator and the comparing operator are applied for the image reconstructing, and feature-level image fusing is accomplished in practice. In contrast, it is better than the effect of other methods.

AB - The character of MRA (Multi-resolution Analysis) of wavelet transform can make the image be divided into different frequency spaces, so the image information in different frequency spaces can be processed and the features of the original image can be controlled. Wavelet transform is used widely in image compressing, edge detecting, noise filtering, image fusing etc. Based on Mallat fast algorithm in this paper, the noise of the image is filtered with threshold method after decomposing. Then the weighing operator and the comparing operator are applied for the image reconstructing, and feature-level image fusing is accomplished in practice. In contrast, it is better than the effect of other methods.

KW - Image fusion

KW - MRA (multi-resolution analysis)

KW - Wavelet transform

UR - https://www.scopus.com/pages/publications/0036442663

U2 - 10.1117/12.470952

DO - 10.1117/12.470952

M3 - 会议文章

AN - SCOPUS:0036442663

SN - 0277-786X

VL - 4919

SP - 289

EP - 292

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Advanced Materials and Devices for Sensing and Imaging

Y2 - 17 October 2002 through 18 October 2002

ER -

Feature-level image fusion technique based on wavelet transform

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Keywords

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