An efficient local contrast and block compressed sensing(BCS) based on multi-focus image fusion algorithm is proposed. Firstly

structural random matrix is used as measurement matrix to obtain a high efficiency sample performance. Secondly

a local contrast measurement in CS domain is proposed to classify the clarity block and the de-focus block

and upon which the larger local contrast block is selected as the fused block. Thirdly

a consistency verification process based on majority filter is introduced to modify the initial fusion CS image. Finally

smoothed projection Landweber (SPL) algorithm is used to reconstruct the fused image to overcome the block artifact. The experimental results show that，compare to the current BCS based image fusion methods

the proposed method achieves good improvement in subjective visual perception quality as well as in objective quantified quality index such as information entropy

mutual information and average gradient for multifocus image fusion.