A New Feature-preserving Nonlinear Anisotropic Diffusion Method for Image Denoising

Zhen Qiu, Lei Yang, Weiping Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We present a new diffusion method for noise reduction and feature preservation.Presently, denoising methods commonly use a first-order derivative to detect edges inorder to achieve a good balance between noise removal and feature preserving.However, if edges are partly lost to a certain extent or contaminated severely bynoise, these methods may not be able to detect them and thus fail to preserve variousfeatures in images. To overcome this problem, we propose a new and moresophisticated feature detector by combining first- and second-order derivatives for anonlinear anisotropic diffusion model. Numerical experiments show that the newdiffusion filter outperforms many popular filters for denoising images containingedges, blobs and ridges and textures made of these features.
Original languageEnglish
Title of host publicationProceedings of the British Machine Vision Conference
EditorsJesse Hoey, Stephen McKenna, Emanuele Trucco
PublisherBMVA Press
ISBN (Electronic)1-901725-43-X
Publication statusPublished - 2 Sept 2011
EventThe 22nd British Machine Vision Conference - University of Dundee, Dundee, United Kingdom
Duration: 29 Aug 20112 Sept 2011


ConferenceThe 22nd British Machine Vision Conference
Abbreviated titleBMVC 2011
Country/TerritoryUnited Kingdom


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