Abstract: Since traditional multi-focus image fusion algorithm has the problem of low contrast ratio,this paper presented a multi-focus image fusion algorithm based on improved weighted method and adaptive pulse coupled neural network (PCNN) in Shearlet domain.Firstly,the source images are decomposed by Shearlet transform to generate a low-frequency subband and a series of high-frequency subbands with different scales in different directions,then the weighted sum of the low-frequency subbands and the absolute value of the difference of the low-frequency subbands are conducted,the weight is calculated by the average gradient,and finally the fused low-frequency subbands are obtained.At the same time,the high-frequency subbands are fused by adaptive PCNN fusion rule,the motivation for PCNN is calculated by sum-modified Laplacian,the linking strength for PCNN is adaptively calculated by the regional spatial frequency of each source images,and the fused high-frequency subbands are obtained according to the ignition map of PCNN.Finally,the fusion image is acquired by the Shearlet inverse transform.One group of artificial simulated multi-focus images named Cameraman and three groups of real multi-focus images named Pepsi,Clock and Peppers are selected respectively for experiments,seven different fusion methods are chosen as a comparison,and four common quality evaluation indexes are used to evaluate the fusion images objectively.The experimental results show that the proposed method has good performance both on subjective vision and objective evaluation.

Key words: Average gradient, Multi-focus image fusion, Pulse coupled neural network, Shearlet transform, Spatial frequency