.png){kind=icon}
.png){kind=icon}
Return
Consensus image method for unknown noise removal
.png){kind=icon}
OA
AI
DOI：10.1016/j.knosys.2013.10.023.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Noise removal has been, and it is nowadays, an important task in computer vision. Usually, it is a previous task preceding other tasks, as segmentation or reconstruction. However, for most existing denoising algorithms the noise model has to be known in advance. In this paper, we introduce a new approach based on consensus to deal with unknown noise models. To do this, different filtered images are obtained, then combined using multifuzzy sets and averaging aggregation functions. The final decision is made by using a penalty function to deliver the compromised image. Results show that this approach is consistent and provides a good compromise between filters. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Consensus
Image noise removal
Unknown noise
Penalty function
Aggregation function
OWA operator
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W
