An Improved Method for Image Denoising Based on Gauss Curvature and Gradient

The Gauss curvature-driven diffusion model for image denoising, which have been proposed by S H Lee and J K Seo, can preserve important structures of image. It uses the Gauss curvature as the conductance function that determines the amount of diffusion. However, the influence of image´s gradient is not considered in the process of denoising. Therefore, the Gauss curvature-driven diffusion model would inevitably blur the edges where there are both high gradient and nonzero Gauss curvature. In this paper, we propose an improved method for image denoising by using a combination of the gradient and the Gauss curvature as the diffusion conductance. Some experiments show that the improved model outperforms the Gauss curvature-driven diffusion model in terms of both mean square error (MSE) and peak signal-to-noise ratio (PSNR).