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AuthorBen Said, Ahmed
AuthorHadjidj, Rachid
AuthorFoufou, Sebti
AuthorAbidi, Mongi
Available date2020-09-24T10:49:24Z
Publication Date2017
Publication Name2017 51st Annual Conference on Information Sciences and Systems, CISS 2017
ResourceScopus
URIhttp://dx.doi.org/10.1109/CISS.2017.7926122
URIhttp://hdl.handle.net/10576/16294
AbstractIn this paper, we present a novel denoising algorithm based on the Rodin-Osher-Fatemi (ROF) model. The goal is to ensure maximum noise removal while preserving image details. To achieve this goal, we developed a new edge detector based on the structure tensor, Non-Local Mean filtering and fuzzy complement. This edge detector is incorporated in the objective function of the ROF model to introduce more control over the amount of regularization allowing more denoising in smooth regions and less denoising when processing edge regions. Experiments on synthetic images demonstrate the efficiency of the edge detector. Furthermore, denoising experiments and comparison with other algorithms show that the proposed method presents good performance in terms of Peak Signal-to-Noise Ratio and Structure Similarity Index.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectEdge detection
Image denoising
Total variation
TitleEdge guided total variation for image denoising
TypeConference Paper
dc.accessType Abstract Only


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