In this paper, we have implemented singular value decomposition to effectively update the value of decomposition, including the basis images. In this paper two dimensional incremental higher order singular value decomposition (HOSVD) is used for image fusion. Incremental higher order SVD will help us to store the images with less storage requirements and will keep the level of the error that must be acceptable in an application. The prime methods used here are HOSVD and its repetitive application. It is already known that singular value matrix obtained by SVD contains the illumination information. Therefore, we will combine this matrix for two different images. Large number of the variations made to this matrix will not affect the other attributes of the image. The incremental approach will be used to divide the image into sub-bands. When the images are separated on LH, HL and HH sub-bands, the effect of fusion will be smoothened by this method.

Singular Value Decomposition, Tensors, Image Fusion, Incremental HOSVD, Reduced HOSVD

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