Aiming at the problem that Pulse Coupled Neural Network ( PCNN) has many parameters and is difficult to optimize,a heterogeneous image fusion method is proposed,which improves the Harris hawk algorithm to optimize PCNN parameters. Firstly,a Harrishawk optimization algorithm for incremental learning of mixed populations is proposed. In the initial evolution stage, the method ofpopulation incremental learning is used to strengthen the ability of the initial population to develop globally,expand the search range ofthe eagle colony,?and better coordinate global development and local development. Secondly, in the development stage, the originalescape energy formula of PCNN is nonlinearized by the excitation function tanh to improve the local mining ability,then the improved algorithm is used to explore the optimal values of the three important parameters of PCNN,and the maximization principle is used to fusethe source images. 21 test functions are selected for simulation experiments,and the results show that the improved algorithm has betteroptimization performance and higher precision than the original algorithm and other algorithms. By selecting four sets of image fusion experiments,the relative image brightness in subjective vision has a certain improvement compared with the original algorithm. In terms ofobjective evaluation,the improved fusion algorithm has improved in many indicators compared with the original fusion algorithm. Thefour sets of fusion results show that the average gradient,clarity and other four indicators have improved. The fusion comparison resultsprove that the proposed method outperforms the original fusion algorithm and other comparison algorithms except for some indicators.