@inproceedings{bcec2b26dd564f33967ed4a99cd96d6e,
title = "Edge guided total variation for image denoising",
abstract = "In 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.",
keywords = "Edge detection, Image denoising, Total variation",
author = "Said, {Ahmed Ben} and Rachid Hadjidj and Sebti Foufou and Mongi Abidi",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 51st Annual Conference on Information Sciences and Systems, CISS 2017 ; Conference date: 22-03-2017 Through 24-03-2017",
year = "2017",
month = may,
day = "10",
doi = "10.1109/CISS.2017.7926122",
language = "English (US)",
series = "2017 51st Annual Conference on Information Sciences and Systems, CISS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2017 51st Annual Conference on Information Sciences and Systems, CISS 2017",
}