The aim of this study was to propose a pulse noise image denoising algorithm of boundary discriminative noise detection coupling difference curvature-driven diffusion to remove high-density impulse noise (≥50%). Based on traditional boundary discriminative noise detection, the noise classification criterion was defined and a modified BDND algorithm was designed to detect the M-BDND model to locate noise edge, accurately identify noise pixels and form noise domain and non-noise domain．Then the domain information of noise pixels was used to form a mask to effectively restore the noise pixels． It only copied the pixels from the observed image in non-noise domain. The diffusion process of noise-pixel domain was governed by difference curvature-driven diffusion model to accomplish image restoration and a numerical analysis was conducted for the difference curvature-driven diffusion model．The experiment results showed that the proposed algorithm had lower mistake rate compared with the current image denoising method for equidensite random impulse and had a good noise reduction effect and was more able to retain the edge and the detail feature of image even if the noise density was as high as 90%． The restoration image of one dimensional row spacing was better and had a high degree of agreement with the real image.