为了提高模糊图像复原后的清晰度，提出蛙跳算法。将蛙群体分成若干个族群，每个族群包含若干只青蛙，每次进化只更新最差青蛙的位置。为防止解空间收缩，对最优个体进行高斯变异算子操作，同时设定阈值策略对蛙跳各维变量进行指导性更新。建立模糊图像复原模型，用蛙跳算法非线性映射特性建立模糊图像与复原的函数关系，使模糊图像复原的解最终收敛于泊松统计的最大似然解。建立了复原评价指标。仿真实验结果表明,与其他算法相比,蛙跳算法复原较清晰，改善百分比最大，同时耗时最少。

In order to obtain clear restoration result from a blurred image, we propose the frog leaping algorithm. The frog population is divided into several groups, each ethnic group includes a number of frogs, and every evolution only updates the worst frog′s position. In order to prevent the solution space from contraction, the best individual is chosen to perform operation with the Gaussian mutation operator. A threshold value strategy is set to guide the update of the variables in various dimensions of frog leaping. The blurred image restoration model is established, in which the relation between blurred and restored images is set up with the nonlinear mapping characteristics of the frog leaping algorithm. The blurred image restoration solution converges on the Poisson statistical maximum likelihood solution. A recovery evaluation system is established. Simulation results show that the frog leaping outperforms other algorithms with the clearer recovery, the highest improvement percentage and the least time consumption.