目的 在传统的去噪模型中,若仅考虑去噪与边缘保护这两个方面,会导致纹理等细节信息丢失,为解决传统模型这方面的缺陷,提出了一种基于拟正态分布的图像去噪模型.方法 提出的模型是以经典的各向异性扩散模型为基础,首先分析了扩散系数在扩散过程中的作用,引入通量函数,做归一化处理,建立新的扩散系数,构造新的扩散模型;然后考虑新模型在去噪过程中,既要有效去噪,又要保护图像的边缘、纹理等细节信息,将扩散系数构造成拟正态分布函数.结果 实验结果表明,在同一实验条件下,新模型的峰值信噪比与经典模型相比提高了28 dB左右,均方差大幅度降低,图像的边缘更加清晰,对比度得到显著增强.结论 提出的新模型能够较稳定地控制扩散过程,使图像在去噪和保边缘、纹理等细节信息方面都达到令人满意的效果,峰值信噪比有了大幅提高,其去噪性能较经典模型更具优越性.

Objective 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. Method 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. Result 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. Conclusion 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.