scholarly journals Synergistic multi-spectral CT reconstruction with directional total variation

2021 ◽  
Vol 379 (2204) ◽  
pp. 20200198 ◽  
Author(s):  
Evelyn Cueva ◽  
Alexander Meaney ◽  
Samuli Siltanen ◽  
Matthias J. Ehrhardt
Keyword(s):  
Total Variation ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Ct Reconstruction ◽  
Spectral Ct ◽  
Iterative Regularization ◽  
Tomographic Image Reconstruction ◽  
Available Energy ◽  
Computational Speed ◽  
Energy Channels

This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel. We propose to fuse these available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyse the use of directional total variation within variational regularization and iterative regularization. Our numerical results on simulated and experimental data show improvements in terms of image quality and in computational speed. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

A compensating total variation image denoising model combining L1 and L2 norm

2020 ◽  
pp. 002072092092330
Author(s):  
Liqiong Zhang ◽  
Min Li ◽  
Xiaohua Qiu
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Isotropic Diffusion ◽  
Model Combining ◽  
L2 Norm ◽  
Staircase Effect

To overcome the “staircase effect” while preserving the structural information such as image edges and textures quickly and effectively, we propose a compensating total variation image denoising model combining L1 and L2 norm. A new compensating regular term is designed, which can perform anisotropic and isotropic diffusion in image denoising, thus making up for insufficient diffusion in the total variation model. The algorithm first uses local standard deviation to distinguish neighborhood types. Then, the anisotropic diffusion based on L1 norm plays the role of edge protection in the strong edge region. The anisotropic and the isotropic diffusion simultaneously exist in the smooth region, so that the weak textures can be protected while overcoming the “staircase effect” effectively. The simulation experiments show that this method can effectively improve the peak signal-to-noise ratio and obtain the higher structural similarity index and the shorter running time.

Total variation with modified group sparsity for CT reconstruction under low SNR

2021 ◽  
pp. 1-18
Author(s):  
Lingli Zhang
Keyword(s):  
Total Variation ◽  
Signal To Noise Ratio ◽  
Gaussian Kernel ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Group Sparsity ◽  
Low Snr ◽  
Overlapping Group Sparsity ◽  
Weighted Factor ◽  
Tv Model

BACKGROUND AND OBJECTIVE: Since the stair artifacts may affect non-destructive testing (NDT) and diagnosis in the later stage, an applicable model is desperately needed, which can deal with the stair artifacts and preserve the edges. However, the classical total variation (TV) algorithm only considers the sparsity of the gradient transformed image. The objective of this study is to introduce and test a new method based on group sparsity to address the low signal-to-noise ratio (SNR) problem. METHODS: This study proposes a weighted total variation with overlapping group sparsity model. This model combines the Gaussian kernel and overlapping group sparsity into TV model denoted as GOGS-TV, which considers the structure sparsity of the image to be reconstructed to deal with the stair artifacts. On one hand, TV is the accepted commercial algorithm, and it can work well in many situations. On the other hand, the Gaussian kernel can associate the points around each pixel. Quantitative assessments are implemented to verify this merit. RESULTS: Numerical simulations are performed to validate the presented method, compared with the classical simultaneous algebraic reconstruction technique (SART) and the state-of-the-art TV algorithm. It confirms the significantly improved SNR of the reconstruction images both in suppressing the noise and preserving the edges using new GOGS-TV model. CONCLUSIONS: The proposed GOGS-TV model demonstrates its advantages to reduce stair artifacts especially in low SNR reconstruction because this new model considers both the sparsity of the gradient image and the structured sparsity. Meanwhile, the Gaussian kernel is utilized as a weighted factor that can be adapted to the global distribution.

Progress in high-resolution CRYO-SEM imaging of viral particles

1996 ◽  
Vol 54 ◽  
pp. 818-819
Author(s):  
Ya Chen ◽  
Geoffrey Letchworth ◽  
John White
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
High Resolution ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Flexible Structures ◽  
Simian Virus ◽  
Topographic Features ◽  
Viral Particles ◽  
Scanning Electron

Low-temperature high-resolution scanning electron microscopy (cryo-HRSEM) has been successfully utilized to image biological macromolecular complexes at nanometer resolution. Recently, imaging of individual viral particles such as reovirus using cryo-HRSEM or simian virus (SIV) using HRSEM, HV-STEM and AFM have been reported. Although conventional electron microscopy (e.g., negative staining, replica, embedding and section), or cryo-TEM technique are widely used in studying of the architectures of viral particles, scanning electron microscopy presents two major advantages. First, secondary electron signal of SEM represents mostly surface topographic features. The topographic details of a biological assembly can be viewed directly and will not be obscured by signals from the opposite surface or from internal structures. Second, SEM may produce high contrast and signal-to-noise ratio images. As a result of this important feature, it is capable of visualizing not only individual virus particles, but also asymmetric or flexible structures. The 2-3 nm resolution obtained using high resolution cryo-SEM made it possible to provide useful surface structural information of macromolecule complexes within cells and tissues. In this study, cryo-HRSEM is utilized to visualize the distribution of glycoproteins of a herpesvirus.

scholarly journals Artefact Removal from EEG Signals using Total Variation De-noising

2020 ◽  
Vol 9 (5) ◽  
pp. 2357-2361
Keyword(s):  
Root Mean Square Error ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Health Issues ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Medical Practitioners ◽  
Artefact Removal ◽  
Two Parameters

Artefacts removing (de-noising) from EEG signals has been an important aspect for medical practitioners for diagnosis of health issues related to brain. Several methods have been used in last few decades. Wavelet and total variation based de-noising have attracted the attention of engineers and scientists due to their de-noising efficiency. In this article, EEG signals have been de-noised using total variation based method and results obtained have been compared with the results obtained from the celebrated wavelet based methods . The performance of methods is measured using two parameters: signal-to-noise ratio and root mean square error. It has been observed that total variation based de-noising methods produce better results than the wavelet based methods.

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