Structure–texture image decomposition using a new non‐local TV‐Hilbert model

Lv, Yehu

doi:10.1049/iet-ipr.2019.0392

Cited by 3 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, the total variation (TV) based models is one of the most popular categories. TV based models are widely used in various image applications, such as image restoration [6][7][8][9][10], image deblurring [11][12][13], image decomposition [14][15][16][17], image segmentation [18][19][20][21] etc. TV based models are powerful due to its ability of preserving edges while there is a main disadvantage named staircase effect [22,23].…”

Section: Introductionmentioning

confidence: 99%

A novel weighted total variation model for image denoising

2021

IET Image Processing

View full text Add to dashboard Cite

Image denoising is a very important problem in image processing field. In order to improve denoising effects and meanwhile keep image structures, a novel weighted total variation (WTV) model is proposed in this paper. The WTV model consists of data fidelity and 1 norm based regularisation terms. In the WTV model, a weight function w in exponential form is incorporated into the regularisation term, which only depends on the given image itself without extra parameters. The nonlinearly monotone formulation of w helps to increase gaps between lower and higher frequencies of images, which is effective to highlight edges and keep textures. For solving the proposed model, the alternating direction method of multipliers is explored and the according convergence is analysed. Compared experiments of TV, HOTV, ATV and TV p models are conducted and the results show the effectiveness and efficiency of the proposed model.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel weighted total variation model for image denoising

2021

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…[48] that neither the l 2norm nor the l 1 -norm is the best choice to measure the oscillatory component since the oscillatory functions don't have small l 2 -and l 1 -norm, which will lead to misclassification in the processing of functional minimisation. Some image structures are sent to the oscillatory component, or conversely, oscillation to the structure component [49][50][51][52][53][54][55]. Moreover, most variational models including those mentioned above are binary decomposition, where the image is decomposed into two components, namely, structure component and oscillatory component.…”

Section: Introductionmentioning

confidence: 99%

“…An alternating direction iteration algorithm is developed to solve this model. For the three subproblems, the iteratively reweighted l 1 algorithm (IRL1) [53], projection algorithm and wavelet soft threshold algorithm are used to numerically solve them, respectively. The main contributions of this study are as follows:…”

Section: Introductionmentioning

confidence: 99%

A non‐convex ternary variational decomposition and its application for image denoising

Tang

Fang

et al. 2021

IET Signal Processing

View full text Add to dashboard Cite

A non‐convex ternary variational decomposition model is proposed in this study, which decomposes the image into three components including structure, texture and noise. In the model, a non‐convex total variation (NTV) regulariser is utilised to model the structure component, and the weaker G and E spaces are used to model the texture and noise components, respectively. The proposed model provides a very sparse representation of the structure in total variation (TV) transform domain due to the use of non‐convex regularisation and cleanly separates the texture and noise since two different weaker spaces are used to model these two components, respectively. In image denoising application, the proposed model can successfully remove noise while effectively preserving image edges and constructing textures. An alternating direction iteration algorithm combining with iteratively reweighted l1 algorithm, projection algorithm and wavelet soft threshold algorithm is introduced to effectively solve the proposed model. Numerical results validate the model and the algorithm for both synthetic and real images. Furthermore, compared with several state‐of‐the‐art image variational restoration models, the proposed model yields the best performance in terms of the peak signal to noise ratio (PSNR) and the mean structural similarity index (SSIM).

show abstract

Fast parallel implementation for total variation constrained algebraic reconstruction technique

Zhang

2022

XST

View full text Add to dashboard Cite

In computed tomography (CT), the total variation (TV) constrained algebraic reconstruction technique (ART) can obtain better reconstruction quality when the projection data are sparse and noisy. However, the ART-TV algorithm remains time-consuming since it requires large numbers of iterations, especially for the reconstruction of high-resolution images. In this work, we propose a fast algorithm to calculate the system matrix for line intersection model and apply this algorithm to perform the forward-projection and back-projection operations of the ART. Then, we utilize the parallel computing techniques of multithreading and graphics processing units (GPU) to accelerate the ART iteration and the TV minimization, respectively. Numerical experiments show that our proposed parallel implementation approach is very efficient and accurate. For the reconstruction of a 2048 × 2048 image from 180 projection views of 2048 detector bins, it takes about 2.2 seconds to perform one iteration of the ART-TV algorithm using our proposed approach on a ten-core platform. Experimental results demonstrate that our new approach achieves a speedup of 23 times over the conventional single-threaded CPU implementation that using the Siddon algorithm.

show abstract

Structure–texture image decomposition using a new non‐local TV‐Hilbert model

Cited by 3 publications

References 31 publications

A novel weighted total variation model for image denoising

A novel weighted total variation model for image denoising

A non‐convex ternary variational decomposition and its application for image denoising

Fast parallel implementation for total variation constrained algebraic reconstruction technique

Contact Info

Product

Resources

About