scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

Computed tomography reconstruction on distributed storage using hybrid regularization approach

2019 ◽  
Vol 33 (06) ◽  
pp. 1950063 ◽  
Author(s):  
Shailendra Tiwari ◽  
Kavkirat Kaur ◽  
Yadunath Pathak ◽  
Shivendraa Shivani ◽  
Kuldeep Kaur
Keyword(s):  
Computed Tomography ◽  
Gaussian Noise ◽  
Distributed Storage ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Noise Robustness ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction

Computed Tomography (CT) is considered as a significant imaging tool for clinical diagnoses. Due to low-dose radiation in CT, the projection data is highly affected by Gaussian noise which may lead to blurred images, staircase effect, loss of basic fine structure and detailed information. Therefore, there is a demand for an approach that can eliminate noise and can provide high-quality images. To achieve this objective, this paper presents a new statistical image reconstruction method by proposing a suitable regularization approach. The proposed regularization is a hybrid approach of Complex Diffusion and Shock filter as a prior term. To handle the problem of prominent Gaussian noise as well as ill-posedness, the proposed hybrid regularization is further combined with the standard Maximum Likelihood Expectation Maximization (MLEM) reconstruction algorithm in an iterative manner and has been referred to as the proposed CT-Reconstruction (CT-R) algorithm here after. Besides, considering the large sizes of image data sets for medical imaging, distributed storage for images have been employed on Hadoop Distributed File System (HDFS) and the proposed MLEM algorithms have been deployed for improved performance.The proposed method has been evaluated on both the simulated and real test phantoms. The final results are compared with the other standard methods and it is observed that the proposed method has many desirable properties such as better noise robustness, less computational cost and enhanced denoising effect.

Minimizing the Embedding Impact Using Network Flow Algorithms

2011 ◽  
Vol 341-342 ◽  
pp. 478-483
Author(s):  
Wan Qi Li ◽  
Heng Wang ◽  
Che Nian ◽  
Huang Wei ◽  
Hong Yao You
Keyword(s):  
Network Flow ◽  
Optimization Problems ◽  
Computational Cost ◽  
Distortion Function ◽  
Cover Image ◽  
Leibler Divergence ◽  
Novel Method ◽  
Low Computational Cost

A novel method of minimizing the embedding impact is proposed in this paper. Optimal embedding is achieved using network flow algorithms by considering the modifications on the cover image as flows of pixels among different states. This method is not an independent steganographic scheme, but rather it minimizes the embedding impact after the embedding process and it’s compatible with the majority of embedding techniques. Due to its dependence on the embedding process, many optimization problems, such as the minimization of a globally interactive distortion function, that are intractable during the embedding process can be solved with relatively low computational cost by rectifying the modifications on the cover image after the embedding process. A distortion function based on Kullback-Leibler divergence is provided as a concrete example to illustrate the basic idea of this method.

REMOVAL OF POISSON NOISE IN MEDICAL IMAGES USING FDCT INTEGRATED WITH RUDIN–OSHER–FATEMI MODEL

2014 ◽  
Vol 26 (06) ◽  
pp. 1450078
Author(s):  
D. Mary Sugantharathnam ◽  
D. Manimegalai
Keyword(s):  
Computed Tomography ◽  
Gaussian Noise ◽  
Medical Images ◽  
Noise Removal ◽  
Poisson Noise ◽  
The Other ◽  
Emission Computed Tomography ◽  
Clinical Images ◽  
Biomedical Images ◽  
Rof Model

This paper introduces a novel approach for accomplishing Poisson noise removal in biomedical images by multiresolution representation. Methods of denoising are described based on three classical methods: (1) Fast Discrete Curvelet Transform (FDCT) with simple soft thresholding, (2) Variance Stabilizing Transform (VST) combined with FDCT where hypothesis tests are made to detect the significant coefficients and (3) The proposed method where the FDCT is integrated with Rudin–Osher–Fatemi (ROF) model. Much of the literature has focused on developing algorithms for the removal of Gaussian noise where the estimation is often done by finding a Curvelet and by thresholding the noisy coefficients. However not much has been done to remove Poisson noise in biomedical images. But in most of the medical images, the recorded data are not modeled by Gaussian noise but is the realization of Poisson process. Hence, in this work, FDCT integrated with ROF model based on VST is proposed. The VST is applied so that the transformed data are homoscedastic and Gaussian. A classical hypothesis testing framework is used to detect the significant coefficients and an iterative scheme is used to reconstruct the final estimate. A central difference total variation term in the discrete ROF model is used. The model is experimented on a large number of clinical images like Computed Tomography (CT) images, X-Ray images, Positron Emission Tomography (PET) images and Single Photon Emission Computed Tomography (SPECT) images and the performances are evaluated in terms of Peak Signal to Noise Ratio (PSNR) and the Universal Quality Index (UQI). The results are compared with those obtained by the other two existing algorithms proposed in the literature. Numerical results show that the proposed algorithm obtains higher PSNR and UQI than the other two methods.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

Image restoration in computed tomography: restoration of experimental CT images

1992 ◽  
Vol 11 (4) ◽  
pp. 546-553 ◽  
Author(s):  
S. Rathee ◽  
Z.J. Koles ◽  
T.R. Overton
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Ct Images

scholarly journals An optimal and low computational cost fractional Newton-type method for solving nonlinear equations

2021 ◽  
pp. 107650
Author(s):  
Giro Candelario ◽  
Alicia Cordero ◽  
Juan R. Torregrosa ◽  
María P. Vassileva
Keyword(s):  
Nonlinear Equations ◽  
Computational Cost ◽  
Type Method ◽  
Low Computational Cost

scholarly journals Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Hsuan-Ming Huang ◽  
Ing-Tsung Hsiao
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Acceleration Techniques ◽  
Reconstruction Methods ◽  
Speed Up ◽  
Phantom Studies

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques.Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively.Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method.Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

Sign in / Sign up

Export Citation Format

Share Document