Iterative image reconstruction using non-local means with total variation from insufficient projection data

2016 ◽  
Vol 24 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Metin Ertas ◽  
Isa Yildirim ◽  
Mustafa Kamasak ◽  
Aydin Akan
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Projection Data ◽  
Iterative Image Reconstruction ◽  
Local Means ◽  
Non Local

Non‐local means improves total‐variation constrained photoacoustic image reconstruction

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Phaneendra K. Yalavarthy ◽  
Sandeep Kumar Kalva ◽  
Manojit Pramanik ◽  
Jaya Prakash
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Local Means ◽  
Non Local

Iterative image reconstruction using modified non-local means filtering for limited-angle computed tomography

2016 ◽  
Vol 32 (9) ◽  
pp. 1041-1051 ◽  
Author(s):  
Hongliang Qi ◽  
Zijia Chen ◽  
Shuyu Wu ◽  
Yuan Xu ◽  
Linghong Zhou
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Iterative Image Reconstruction ◽  
Local Means ◽  
Non Local ◽  
Limited Angle

Iterative image reconstruction for sparse-view CT via total variation regularization and dictionary learning

2019 ◽  
Vol 27 (3) ◽  
pp. 573-590
Author(s):  
Xianyu Zhao ◽  
Changhui Jiang ◽  
Qiyang Zhang ◽  
Yongshuai Ge ◽  
Dong Liang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Dictionary Learning ◽  
Total Variation Regularization ◽  
Iterative Image Reconstruction

scholarly journals An iterative tomosynthesis reconstruction using total variation combined with non-local means filtering

2014 ◽  
Vol 13 (1) ◽  
pp. 65 ◽  
Author(s):  
Metin Ertas ◽  
Isa Yildirim ◽  
Mustafa Kamasak ◽  
Aydin Akan
Keyword(s):  
Total Variation ◽  
Local Means ◽  
Non Local

scholarly journals Penalized-Likelihood PET Image Reconstruction Using Similarity-Driven Median Regularization

Tomography ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 158-174
Author(s):  
Xue Ren ◽  
Ji Eun Jung ◽  
Wen Zhu ◽  
Soo-Jin Lee
Keyword(s):  
Image Reconstruction ◽  
Tomographic Reconstruction ◽  
Penalized Likelihood ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Local Means ◽  
Positron Emission ◽  
Negative Effect ◽  
Non Local ◽  
Spatially Variant

In this paper, we present a new regularized image reconstruction method for positron emission tomography (PET), where an adaptive weighted median regularizer is used in the context of a penalized-likelihood framework. The motivation of our work is to overcome the limitation of the conventional median regularizer, which has proven useful for tomographic reconstruction but suffers from the negative effect of removing fine details in the underlying image when the edges occupy less than half of the window elements. The crux of our method is inspired by the well-known non-local means denoising approach, which exploits the measure of similarity between the image patches for weighted smoothing. However, our method is different from the non-local means denoising approach in that the similarity measure between the patches is used for the median weights rather than for the smoothing weights. As the median weights, in this case, are spatially variant, they provide adaptive median regularization achieving high-quality reconstructions. The experimental results indicate that our similarity-driven median regularization method not only improves the reconstruction accuracy, but also has great potential for super-resolution reconstruction for PET.

Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

2020 ◽  
Vol 13 (4) ◽  
pp. 14-31
Author(s):  
Nikita Joshi ◽  
Sarika Jain ◽  
Amit Agarwal
Keyword(s):  
Magnetic Resonance ◽  
Total Variation ◽  
Similarity Index ◽  
Noise Removal ◽  
Magnetic Resonance Images ◽  
Variation Method ◽  
Mr Images ◽  
Denoising Method ◽  
Local Means ◽  
Non Local

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

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