As noise filtering of a star image has a high demand for reserving details of star edge, a new star map noise filtering method on a regularization influence function diffusion model was proposed based on Tukey diffusion model and modified Perona-Malik model. The boundary point set was extracted by a derivative operator and the map noise was processed by filtering with the space distribution characteristics of the original pixel and the noise pixel in the images. Moreover, the image edge was recovered by a given boundary condition. Due to avoiding Variance Stabilization(VS) transform,it could process the Gaussian noise directly. Simulation experiments on a common image and a star map with Gaussian noise show that this method has good capability of noise filtering and can effectively reserve the edges of feature images. Compared with common diffusion function algorithm, the average error is reduced by 13.6% and the Peak Signal to Noise Ratio(PSNR) is improved by 6.1%. Filtering performance of the proposed method is better than that of common diffusion function method, especially suitable for noise filtering of star maps.