A Hybrid Model for Image Denoising Combining Modified Isotropic Diffusion Model and Modified Perona-Malik Model

In this paper, a hybrid image denoising algorithm based on directional diffusion is proposed. Specifically, we developed a new noise-removal model by combining the modified isotropic diffusion model and the modified Perona-Malik (PM) model. The novel hybrid model can adapt the diffusion process along the tangential direction of edges in the original image via a new control function based on the patch similarity modulus. In addition, the patch similarity modulus is used as the new structure indicator for the modified Perona-Malik model. The feature of second-order directional derivative of edge's tangential direction allows the proposed model to reduce the aliasing and the noise around edge during edge preserving smoothing. The proposed method is thus able to efficiently preserve the edges, textures, thin lines, weak edges, and fine details, meanwhile preventing the staircase effects. Computer experiments on synthetic image and nature images demonstrate that the proposed model achieves a better performance than the conventional partial differential equations models and some recent advanced models.