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A Review of Image Fusion Methods.

Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.3, No. 3)

Publication Date:

Authors : ; ;

Page : 1462-1467

Keywords : Image fusion; Discrete cosine transform; Discrete wavelet transform; higher order singular value decomposition (HOSVD); sigmoid function.;

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Abstract

Image fusion is the process of combining relevant information from two or more images into a single image.. The resulting image will be more informative than any of the input images. There are many image fusion methods. This paper present a literature review on some of the image fusion techniques for image fusion and propose novel higher order singular value decomposition (HOSVD) based image fusion algorithm.Image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor.Image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor.Image fusion is the process of combining relevant information from two or more images into a single image.. The resulting image will be more informative than any of the input images. There are many image fusion methods. This paper present a literature review on some of the image fusion techniques for image fusion and propose novel higher order singular value decomposition (HOSVD) based image fusion algorithm.Image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor.Image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor.

Last modified: 2014-05-26 17:25:13