Noise reduction of low-dose computed tomography using the multi-resolution total variation minimization algorithm

2013 ◽  
Author(s):  
Cheng-Ting Shih ◽  
Shu-Jun Chang ◽  
Yan-lin Liu ◽  
Jay Wu
Keyword(s):  
Computed Tomography ◽  
Noise Reduction ◽  
Total Variation ◽  
Low Dose ◽  
Minimization Algorithm ◽  
Total Variation Minimization ◽  
Low Dose Computed Tomography

Adaptive noise reduction toward low-dose computed tomography

2003 ◽  
Author(s):  
Hongbing Lu ◽  
Xiang Li ◽  
Lihong Li ◽  
Dongqing Chen ◽  
Yuxiang Xing ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Noise Reduction ◽  
Low Dose ◽  
Adaptive Noise ◽  
Low Dose Computed Tomography

scholarly journals Computed tomography noise reduction based on total variation minimization and morphological component analysis

2021 ◽  
Author(s):  
Aryan Khodabandeh
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Low Dose ◽  
Denoising Method ◽  
Decomposition Approach ◽  
Total Variation Minimization ◽  
Morphological Component Analysis ◽  
Image Separation ◽  
X Ray Computed ◽  
Harmful Radiation

X-ray Computed Tomography (CT) scans, while useful, emit harmful radiation which is why low-dose image acquisition is desired. However, noise corruption in these cases is a difficult obstacle. CT image denoising is a challenging topic because of the difficulty in modeling noise. In this study, we propose taking an image decomposition approach to removing noise from low-dose CT images. We model the image as the superposition of a structure layer and a noise layer. Total Variation (TV) minimization is used to learn two dictionaries to represent each layer independently, and sparse coding is used to separate them. Finally, an iterative post-processing stage is introduced that uses image-adapted curvelet dictionaries to recover blurred edges. Our results demonstrate that image separation is a viable alternative to the classic K-SVD denoising method.

scholarly journals Computed tomography noise reduction based on total variation minimization and morphological component analysis

2021 ◽  
Author(s):  
Aryan Khodabandeh
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Low Dose ◽  
Denoising Method ◽  
Decomposition Approach ◽  
Total Variation Minimization ◽  
Morphological Component Analysis ◽  
Image Separation ◽  
X Ray Computed ◽  
Harmful Radiation

X-ray Computed Tomography (CT) scans, while useful, emit harmful radiation which is why low-dose image acquisition is desired. However, noise corruption in these cases is a difficult obstacle. CT image denoising is a challenging topic because of the difficulty in modeling noise. In this study, we propose taking an image decomposition approach to removing noise from low-dose CT images. We model the image as the superposition of a structure layer and a noise layer. Total Variation (TV) minimization is used to learn two dictionaries to represent each layer independently, and sparse coding is used to separate them. Finally, an iterative post-processing stage is introduced that uses image-adapted curvelet dictionaries to recover blurred edges. Our results demonstrate that image separation is a viable alternative to the classic K-SVD denoising method.

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