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.
Publication Year: 2017
Publication Date: 2017-03-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 1
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot