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Image Fusion Methods

Authors: Zheng Liu; Yufeng Zheng; Erik Blasch;

Image Fusion Methods

Abstract

Image fusion at the pixel level can be implemented as simply as an arithmetic average; however, there are many techniques that improve on the simple methods. For example, a weighted sum of two or more input images is a valid image fusion. Principal component analysis (PCA) can be applied to decide the weights of input images (i.e., which input is more significant). In these methods, input images are directly combined in the spatial (pixel) domain. This chapter fully describes two multiscale (also called multiresolution) fusion techniques: pyramids and wavelets. The multiscale fusion processes are usually performed in the transformed domain. Color image fusion and multi-image (three or more) fusion are introduced as examples. The chapter also highlights recent techniques that extend the wavelet concept, including bandelets and contourlets for image fusion. Finally, a set of fusion examples is presented for multimodal and multiscale image fusion.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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