Authors: Juha Tiirola
Publish Date: 2016/06/20
Volume: 57, Issue: 1, Pages: 56-74
Abstract
In this paper a new variational image denoising model is proposed The new model could be seen to be a twostep method In the first step structure tensor analysis is used to infer something about the local geometry The eigenvectors and the eigenvalues of the structure tensor are used in the construction of the denoising energy In the second step the actual variational denoising takes place The steps are coupled in the sense that the energy expression is built using the underlying image not the data Two variable exponents are incorporated into the regularizer in order to reduce the staircasing effect which is often present in the methods based on the firstorder partial derivatives and to increase smoothing along the image boundaries In addition two pointwise weight functions try to help to preserve smallscale details In the theoretical part the existence of a minimizer of a weak form of the original energy is considered In the numerical part an algorithm based on iterative minimization is presented and the numerical experiments demonstrate the possible advantages of the new model over some existing variational and partial differential equations methods
Keywords: