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Multi-resolution image data fusion using 2-D discrete wavelet transform and self-organizing neural networks

Published: 16 June 2004 Publication History

Abstract

In recent years, many solutions to multi-resolution image data fusion have been proposed; however, it is difficult to simulate the human ability of image fusion when algorithms of image processing are piled up merely. On the basis of the review of researches on psychophysics and physiology of human vision, this paper presents an effective multi-resolution image data fusion methodology, which is based on discrete wavelet transform theory and self-organizing neural network, to simulate the processes of images recognition and understanding implemented in the human vision system. Through the two-dimensional wavelet transform, original images can be decomposed in to different types of details and levels. The integration rule can be built using self-organizing neural networks, just like the automatic work in human brain. As an example, the model is applied to images obtained by Cyclone Center Locating Satellite System (CCLSS). The effectiveness of the proposed model is demonstrated via results comparison with several other image fusion methods.

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cover image ACM Conferences
VRCAI '04: Proceedings of the 2004 ACM SIGGRAPH international conference on Virtual Reality continuum and its applications in industry
June 2004
493 pages
ISBN:1581138849
DOI:10.1145/1044588
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2004

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Author Tags

  1. discrete wavelet transform
  2. multi-resolution image data fusion
  3. self-organizing neural network

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