An Adaptive Image Denoising Model Based on Nonlocal Diffusion Tensor

When denoising with the method of Weickert´s anisotropic diffusion equation, the textures and details will be compromised. A fidelity term is added into Weickert´s equation. The coefficient of fidelity term will vary adaptively with the instant image, which makes that the diffusion term and the fidelity term come to a better compromise. Otherwise, when deciding the edge directions, because of the strong smoothness of linear Gaussian function, a few other edge directions hiding in the main direction will be lost. To preserve these detailed edge directions, Gaussian kernel is substituted for nonlinear wavelet threshold. In addition, in order to preserving the textures and details as much as possible, a nonlocal diffusion tensor was introduced and the two eigenvalues are reset by combining the two methods: edge enhancing diffusion and coherence enhancing diffusion. Experiments show that the new model has obvious effect in preserving textures and details.