This paper studies the traits of Ant Colony Algorithm and BP neural network, at the same time it combines the ant colony optimization algorithm with BP neural network and applies them at the image restoration. This algorithm solves some problems of BP, such that the BP algorithm gets in local minimum easily, the speed of convergence is slowly and sometimes brings oscillation effect etc. that is reason the quality of restored image can be improved significantly. Besides, the article details ACO-BP algorithm’s theory and steps, and apply the improved algorithm in the image restoration. which reduces the MSE(Mean Square Error) of the optimization algorithm, and makes the speed of convergence of BP neural network faster. This algorithm is validated validly by the method of Simulation .