A novel multi-focus image fusion algorithm based on image sparse representation theory and on the Human Visual System is introduced in this paper. For the new algorithm

the related images are partitioned into image patches. The learning for an adaptive dictionary is implemented by an iterated processing. All image patches are decomposed by the orthogonal matching pursuit (OMP) algorithm

and the coefficients with prominent properties are selected for image patch reconstruction. The reconstructed patches are realigned according to their partition order and the overlapped patches are averaged to get the fused image.The experiment results demonstrate that the proposed algorithm maintains the property of the state-of-the-art typical algorithm. In addition

with a noisy input

the proposed method can generate noiseless results and the objective criteria outperform the typical algorithm when noise variance increasing.