Sum-modified-Laplacian-based multifocus image fusion algorithm in cycle spinning sharp frequency localized contourlet transform (SFLCT) domain is proposed in this paper. SFLCT successfully reduces significant amount of aliasing components of the original contourlet which are located far away from the desired support. However

downsamplers and upsamplers presented in directional filter banks of SFLCT make it not shift-invariant and easily cause pseudo-Gibbs phenomena around singularities. Thus

we apply cycle spinning to compensate for the lack of translation invariance property. Furthermore

typical measurements for multifocus image fusion in spatial domain are introduced into contourlet domain and Sum-modified-Laplacian (SML)

evidenced in this paper with the best capability to distinguish coefficients is from the clear parts or blurry parts of images

is employed in SFCLT subbands as measurement to compose coefficients of fused images. Experimental results demonstrate the proposed fusion method outperforms block-based spatial SML method

typical cycle spinning wavelet and shift-invariant wavelet methods

and typical cycle spinning contourlet methods in term of visual appearance and objective criteria for multifocus images.