An Image Decomposition Model Using the Dual Method and H−1 Norm
Qibin Fan + and Tao Zhang
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
Abstract. In image denoising process, it is difficult to separate texture from noise. In order to separate them, we should know their different characteristics, or we can use some metrics (such as norms) to distinguish them. In this paper, we propose a new model which decomposes an initial image f into three component: structure part u , texture part v , and noise part w . And we use the H−1 norm which is investigated in the work of Aujol and Chamboll to separate texture from noise.
Keywords: metric, total variation, Sobolev space, H−1norm, texture, dual transform
In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).