Dynamic PET reconstruction using the kernel method with non-local means denoising

2021 ◽  
Vol 68 ◽  
pp. 102673
Author(s):  
Hsuan-Ming Huang
Keyword(s):  
Kernel Method ◽  
Dynamic Pet ◽  
Local Means ◽  
Non Local ◽  
Pet Reconstruction

scholarly journals Non-Local Means Denoising of Dynamic PET Images

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e81390 ◽  
Author(s):  
Joyita Dutta ◽  
Richard M. Leahy ◽  
Quanzheng Li
Keyword(s):  
Dynamic Pet ◽  
Local Means ◽  
Non Local

Denoising of dynamic PET images using a multi-scale transform and non-local means filter

2018 ◽  
Vol 41 ◽  
pp. 69-80 ◽  
Author(s):  
Hajer Jomaa ◽  
Rostom Mabrouk ◽  
Nawres Khlifa ◽  
Frédéric Morain-Nicolier
Keyword(s):  
Dynamic Pet ◽  
Multi Scale ◽  
Local Means ◽  
Non Local

Fuzzy diffusion PET reconstruction algorithm based on anatomical non-local means prior

2013 ◽  
Vol 33 (9) ◽  
pp. 2627-2630
Author(s):  
Hong SHANGGUAN ◽  
Yi LIU ◽  
Quan ZHANG ◽  
Zhiguo GUI
Keyword(s):  
Reconstruction Algorithm ◽  
Local Means ◽  
Non Local ◽  
Pet Reconstruction

scholarly journals Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

2021 ◽  
Vol 18 (6) ◽  
pp. 2903
Author(s):  
Seong-Hyeon Kang ◽  
Ji-Youn Kim
Keyword(s):  
Light Microscopy ◽  
Noise Reduction ◽  
Natural Image ◽  
Search Window ◽  
Local Means ◽  
Image Characteristics ◽  
Non Local ◽  
Spatial Quality ◽  
Evaluation Parameters ◽  
Color Channels

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

Reproducing kernel method to solve non-local fractional boundary value problem

2021 ◽  
Author(s):  
Raziye Mohammad Hosseiny ◽  
Tofigh Allahviranloo ◽  
Saeid Abbasbandy ◽  
Esmail Babolian
Keyword(s):  
Boundary Value Problem ◽  
Reproducing Kernel ◽  
Kernel Method ◽  
Boundary Value ◽  
Fractional Boundary Value Problem ◽  
Reproducing Kernel Method ◽  
Non Local

Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

2021 ◽  
pp. 2150051
Author(s):  
S. Elavaar Kuzhali ◽  
D. S. Suresh
Keyword(s):  
Image Denoising ◽  
Image Patch ◽  
The Past ◽  
Local Means ◽  
Hybrid Optimization Algorithm ◽  
Proposed Model ◽  
Processing Step ◽  
Non Local ◽  
Chicken Swarm Optimization ◽  
Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

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