A novel weighted total variation model for image denoising. Issue 12 (16th May 2021)

• Record Type: Journal Article
• Authors: Li, Meng‐Meng; Li, Bing‐Zhao
• Is Part Of: IET image processing. Volume 15:Issue 12(2021)
• Page Start: 2749
• Page End: 2760
• Publication Date: 2021-05-16
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12259
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Image denoising is a very important problem in image processing field. In order to improve denoising effects and meanwhile keep image structures, a novel weighted total variation (WTV) model is proposed in this paper. The WTV model consists of data fidelity and ℓ 1 norm based regularisation terms. In the WTV model, a weight function w in exponential form is incorporated into the regularisation term, which only depends on the given image itself without extra parameters. The nonlinearly monotone formulation of w helps to increase gaps between lower and higher frequencies of images, which is effective to highlight edges and keep textures. For solving the proposed model, the alternating direction method of multipliers is explored and the according convergence is analysed. Compared experiments of TV, HOTV, ATV and TV p models are conducted and the results show the effectiveness and efficiency of the proposed model.