Image Denoising Using TV-Stokes Equation with an Orientation-Matching Minimization

Abstract. In this paper, we propose an orientation-matching minimization for denoising digital images with an additive noise. Inspired by the two-step algorithm in the TV-Stokes denoising process [1,2,3], the regularized tangential vector field with the zero divergence condition is used in the first step. The present work suggests a different approach in order to reconstruct a denoised image in the second step. Namely, instead of finding an image that fits the regularized normal direction from the first step, we minimize an orientation between the image gradient and the regularized normal direction. It gives a nonlinear partial differential equation (PDE) for reconstructing denoised images, which has the diffusivity depending on an orientation of a regularized normal vector field and the weighted self-adaptive force term depending on the direction between the gradient of an image and the vector field. This allows to obtain a denoised image which has sharp edges and smooth regi...

• Type: Conference
• Year: 2009
• Where: SCALESPACE
• Authors: Xue-Cheng Tai, Sofia Borok, Jooyoung Hahn
• Keywords: Computer Vision, Image, Regularized Normal Direction, SCALESPACE 2009, Vector field