Open AccessNoise reduction with low dose CT data based on a modified ROF model
Author(s) -
Yining Zhu,
Mengliu Zhao,
Yunsong Zhao,
Hongwei Li,
Peng Zhang
Publication year - 2012
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.20.017987
Subject(s) - noise (video) , noise reduction , image quality , image noise , shot noise , optics , computer science , tomography , iterative reconstruction , reduction (mathematics) , materials science , artificial intelligence , computer vision , physics , image (mathematics) , mathematics , detector , geometry
In order to reduce the radiation exposure caused by Computed Tomography (CT) scanning, low dose CT has gained much interest in research as well as in industry. One fundamental difficulty for low dose CT lies in its heavy noise pollution in the raw data which leads to quality deterioration for reconstructed images. In this paper, we propose a modified ROF model to denoise low dose CT measurement data in light of Poisson noise model. Experimental results indicate that the reconstructed CT images based on measurement data processed by our model are in better quality, compared to the original ROF model or bilateral filtering.