In the traditional de-noising model

considering only image de-noising and edge protection will lead to loss of detailed information. To address these shortcomings of the traditional models

we present an image de-noising model based on matching normal distribution. The proposed model is based on the classical anisotropic diffusion model. The effect of the diffusion coefficient in the diffusion process is first analyzed

and the flux function processed by normalization is introduced into the establishment of a new diffusion coefficient. The novel diffusion model is then built. The newly established model deals with both de-noising performance and the protection of the edge and texture of the image. Thus

another model is proposed to build the diffusion coefficient into a normal distribution function. Simulation results indicate that the peak signal-to-noise ratio is improved by 28 dB

the mean square error decreases sharply

the image edge is clearer

and the contrast is enhanced sharply. The novel proposed model can handle the diffusion process and maintain good de-noising performance and edge protection. The detailed information of the texture is satisfactory

and the peak signal-to-noise ratio is improved drastically. Therefore

the performance of the proposed model is better than that of the classical model.