A novel higher order singular value decomposition (HOSVD)-

based image fusion algorithm is proposed. The key points are given as follows:

1) Since 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 2) the sum of absolute values of the coefficients

(SAVC) from HOSVD of subtensors is employed for activity-level

measurement to evaluate the quality of the related image patch and 3) a

novel sigmoid-function-like coefficient-combining scheme is applied to construct

the fused result. Experimental results show that the proposed algorithm

is an alternative image fusion approach.-A novel higher order singular value decomposition (HOSVD)-

based image fusion algorithm is proposed. The key points are given as follows:

1) Since 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　2) the sum of absolute values of the coefficients

(SAVC) from HOSVD of subtensors is employed for activity-level

measurement to evaluate the quality of the related image patch　and 3) a

novel sigmoid-function-like coefficient-combining scheme is applied to construct

the fused result. Experimental results show that the proposed algorithm

is an alternative image fusion approach.