Edge‐preserving image denoising. Issue 8 (1st August 2018)

• Record Type: Journal Article
• Authors: Guo, Fenghua; Zhang, Caiming; Zhang, Mingli
• Is Part Of: IET image processing. Volume 12:Issue 8(2018)
• Page Start: 1394
• Page End: 1401
• Publication Date: 2018-08-01
• Subjects: image denoising -- regression analysis; high‐frequency components -- low‐ frequency components -- edge‐preserving image denoising algorithm -- neighbourhood regression method; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0880
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In image denoising, high‐frequency components are more notable to the human eyes than low‐frequency components. While high‐frequency components contain more variations and represent the detailed textures, the reconstructions of these components are much harder and it is a remaining challenge in image denoising. In this study, a novel edge‐preserving image denoising algorithm is proposed, it treats the low‐ and high‐frequency components of the image separately. For restoration of high‐frequency components, a neighbourhood regression method is proposed. An energy minimisation function is developed to combine the low‐ and high‐frequency components into one model. Experiments show that the proposed method outperforms the state‐of‐the‐art methods in peak signal‐to‐noise ratio, edges preservation and visual performance.