关键词: 高斯-泊松混合噪声, 调和模型, 全变差图像恢复模型, 图像恢复, 变分法, 偏微分方程, 数值仿真

Abstract:

In order to effectively suppress Gaussian-Poisson mixed noise, the edge region detail information and the Kullback-Leibler divergence can not be saved effectively for the harmonic model as a fidelity term（KL fidelity term）. The noise has the disadvantage of “step effect”. An image restoration variation model for Gaussian-Poisson mixed noise denoising is proposed. This model uses augmented Lagrangian algorithm for numerical implementation. Firstly, the harmonic model and the full variational model are fused according to the proportion, and the advantages of the two models are combined to enhance the denoising performance of the model. Then the Kullback-Leibler divergence is used as the guarantee. The true terms and fidelity terms are  mixed according to the proportion, which can effectively remove the Gauss-Poisson mixed noise and protect the edge details of the image. Finally, multiple images with different mixed noises are  used for comparison experiments. The peak signal-to-noise ratio and structural similarity index are used to evaluate the restoration effect of the image. Experimental results show that the peak signal-to-noise ratio and structural similarity of the model are larger than the Total Variation Kullback-Leible（TV-KL） model using the Kullback-Leibler divergence as a fidelity term, the improved MS model（Mix Regularization Term, MRT）, and Mix Fidelity Term（MFT）, and the calculated CPU time is shorter, and the denoising effect is significantly improved. This model has better denoising performance, effectively maintains the information of image details and texture features, and obtains more ideal visual effects. It not only improves the image quality, but also is effectively validated objectively. Ray image denoising has certain application value.

Key words: Gaussian-Poisson mixed noise, harmonic model, total variation Kullback-Leibler model, image restoration, variational method, partial differential equation, numerical simulation