Compressive near-field millimeter wave imaging algorithm based on Gini index and total variation mixed regularization

A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data.The Gini index(GI)has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood,Scaling,Rising Tide,Cloning,Bill Gates,and Babies.By combining the total variation(TV)operator,the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed.In addition,the corresponding algorithm based on a primal-dual framework is also proposed.Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l1-TV mixed regularization algorithm.

Image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography

Linear scan computed tomography （LCT） is of great benefit to online industrial scanning and security inspection due to its characteristics of straight-line source trajectory and high scanning speed. However, in practical applications of LCT, there are challenges to image reconstruction due to limited-angle and insufficient data. In this paper, a new reconstruction algorithm based on total-variation （TV） minimization is developed to reconstruct images from limited-angle and insufficient data in LCT. The main idea of our approach is to reformulate a TV problem as a linear equality constrained problem where the objective function is separable, and then minimize its augmented Lagrangian function by using alternating direction method （ADM） to solve subproblems. The proposed method is robust and efficient in the task of reconstruction by showing the convergence of ADM. The numerical simulations and real data reconstructions show that the proposed reconstruction method brings reasonable performance and outperforms some previous ones when applied to an LCT imaging problem.

Dark channel prior based blurred image restoration method using total variation and morphology

The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical ap- plications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation （TV） and image morphology transform （specifically top-hat transform and bottom- hat transform） have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The es- timated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition.

Image decomposition and staircase effect reduction based on total generalized variation

Total variation （TV） is widely applied in image process-ing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-cal ed staircase effect. In order to reduce the staircase effect and preserve the edges when textures of image are extracted, a new image decomposition model is proposed in this paper. The proposed model is based on the to-tal generalized variation method which involves and balances the higher order of the structure. We also derive a numerical algorithm based on a primal-dual formulation that can be effectively imple-mented. Numerical experiments show that the proposed method can achieve a better trade-off between noise removal and texture extraction, while avoiding the staircase effect efficiently.

Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration

This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.

Novel image restoration model coupling gradient fidelity term based on adaptive total variation

A novel image restoration model coupling with a gradient fidelity term based on adaptive total variation is proposed in this paper. In order to choose proper parameters, the selection criteria were analyzed theoretically, and a simple scheme to demonstrate its validity was adopted experimentally. To make fair comparisons of performances of three models, the same numerical algorithm was used to solve partial differential equations. Both the international standard test image on Lena and HR image of CBERS-02B of Dalian city were used to verify the performance of the model. Experimental results illustrate that the new model not only preserved the edge and important details but also alleviated the staircase effect effectively.

Multiquadric Radial Basis Function Approximation Scheme for Solution of Total Variation Based Multiplicative Noise Removal Model

This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimization functional and to show its application to image denoising containing multiplicative noise.These capabilities used within the proposed algorithm have not only the quality of image denoising,edge preservation but also the property of minimization of staircase effect which results in blocky effects in the images.It is worth mentioning that the recommended method can be easily employed for nonlinear problems due to the lack of dependence on a mesh or integration procedure.The numerical investigations and corresponding examples prove the effectiveness of the recommended algorithm regarding the robustness and visual improvement as well as peak-signal-to-noise ratio(PSNR),signal-to-noise ratio(SNR),and structural similarity index(SSIM)corresponded to the current conventional TV-based schemes.

基于稀疏约束TV最小化的CT图像重建

稀疏角度计算机断层扫描成像(computer tomography,CT)图像重建是一种降低CT扫描辐射剂量的重要方式。而经典的用于稀疏角度CT图像重建的广义迭代软阈值(generalizationoftheiterativesoft-thresholding,GIST)算法收敛速度慢,不能满足临床对检查结果实时性的要求。全变分(totalvariation,TV)最小化算法在稀疏角度下既能减少条纹伪影又能较好地保留目标边界。文章提出稀疏约束的全变分算法,利用交替方向乘子法(alternatedirectionmultipliermethod,ADMM),将重建优化问题分解为2个子问题之和,有效提升了重建效果的同时提高了重建速度。实验结果表明:所提出的方法对数字模型图像和实际临床腹部图像都实现了高质量的图像重建,收敛速度较传统的同步迭代方法有大幅提升。

Weighted Variational Minimization Model for Wavelet Domain Inpainting with Primal-Dual Method

To preserve the edges and details of the image,a new variational model for wavelet domain inpainting was proposed which contained a non-convex regularizer. The non-convex regularizer can utilize the local information of image and perform better than those usual convex ones. In addition, to solve the non-convex minimization problem,an iterative reweighted method and a primaldual method were designed. The numerical experiments show that the new model not only gets better visual effects but also obtains higher signal to noise ratio than the recent method.

Low-rank matrix recovery with total generalized variation for defending adversarial examples

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image structure.Taking advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural networks.However,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its over-smoothing.In our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery model.In the proposed model,TGV is used to better reconstruct texture information without over-smoothing.The reweighted nuclear norm and Li-norm can enhance the global structure information.Thus,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the image.To solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of multipliers.Experimental results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

稀疏角度CT图像重建的Huber-TV正则化方法

对于稀疏角度下的投影数据,计算机断层扫描重建图像容易出现分辨率低、伪影较多的问题,难以满足工业及医学诊断要求。文中从迭代重建的角度出发,提出一个结合全变分(TV)和Huber函数(Huber-TV)的CT重建方法。该方法利用Huber函数替代传统全变分模型中的L1范数,在合理控制函数阈值的条件下,充分利用Huber函数的线性部分对大于阈值的梯度图像进行较轻的惩罚,以保持图像边缘连续性;再结合二次项对小于阈值的梯度图像进行较大的惩罚,以抑制图像中不连续梯度跳跃。新模型目标函数的光滑性可以使得梯度下降法快速收敛到最优值,避开传统全变分模型中的次梯度计算,从而降低计算复杂度并加快迭代速度。实验结果表明,在稀疏角度重建条件下,与传统TV模型相比,Huber-TV模型的均方根误差降低19%,信噪比提升22.33 dB,说明所提方法高效可行。

基于分数阶全变分和低秩正则化的彩色图像去模糊方法

针对现有的彩色图像去模糊过程中存在色彩失衡、阶梯效应和伪影等现象,提出了一种基于分数阶全变分和低秩正则的图像去模糊优化方法。首先,将传统的RGB彩色图像转换到YCbCr颜色空间,利用其亮度通道特征解决色彩失衡问题;其次,利用分数阶全变分的特征消除图像恢复任务中出现的阶梯效应,并且引入加权核范数低秩正则进一步抑制伪影及噪声;最后,利用交替方向乘子法设计出高效的求解方法,通过迭代优化得到纯净图像的最优估计。对彩色图像测试的实验结果表明,所提出的方法对图像去模糊任务取得较好的视觉恢复效果,客观评价指标良好。

基于TV模型的改进算法在图像修复中的应用

基于TV(total variation)模型的修复算法有较好的恢复效果,但对参数的选取比较敏感,且运算量大。文章提出了基于TV模型的改进的自适应算法,可根据破损区域外部参考像素对待修补点的相关度,通过设置不同的参数和权值,将不同形状的待修复区域所用的不同算法统一表示,使其应用范围更广、速度更快;此外,在迭代过程中,设置不同的参数以解决参数选取的敏感问题,从而达到更好的修复效果。实验表明,该算法能高效、稳定地处理破损区域的图像信息。

综合自适应阈值与多尺度的TV图像修复方法

基于TV模型的图像修复算法具有较好的修复效果,但其对参数的选取较敏感,且运算量较大。该文提出了一种综合自适应阈值与多尺度的TV图像修复算法,该方法不仅可以提高TV图像修复模型的修复稳定性,还可以进一步压缩运算量,提高修复速度。

改进TV复原模型的彩色-灰度图像变换方法

针对彩色图像到灰度图像的变换这一问题,提出了一种新的基于TV(全变分)复原模型的新方法。利用Sapiro等所提出的矢量图像的水平集概念,结合全变分复原,建立新的变换模型,实现算法。实验表明,所提方法实现的彩色一灰度图像变换,不仅很好地解决了原来传统变换不能保持形状的问题,并且能解决Sapiro等所提出方法的边缘模糊化缺点,使变换后图像的边缘锐利程度得到了较大的提高。

基于自适应中值滤波器和TV修复的椒盐噪声去除(英文)

提出一种两步式椒盐噪声去除方法.第一步利用自适应中值滤波器来识别出被噪声污染的候选点;第二步利用一种边缘保持技术(全变差图像修复)来恢复出被噪声污染的点.由于椒盐噪声点的灰度值和原始像素灰度独立,所以在采用恢复技术时不使用噪声点自身的灰度信息.在噪声率不很高的情况下,这种方法可以获得比现有最好方法更高的信噪比;当噪声率高达70%以上时,该方法的信噪比与现有方法非常接近,但是主观视觉效果(例如边缘保持能力)更佳.

基于TV与SSIM的图像质量评价方法

提出一种基于全变分(TV)模型与结构相似度(SSIM)的图像质量评价方法。对待评价图像进行主动定量加噪,得到降质图像,利用自适应的TV去噪模型得到消噪图像,采用SSIM方法对待评价图像与消噪图像进行全参考评价,得到待评价图像的无参考评价指标。采用标准测试图像和LIVE库的降质图像进行实验,结果表明,该方法可在无参考图像的条件下对图像质量进行评估,评价结果与主观评价结果具有较高的一致性。

模拟一维非定常流动的 FVM-TVD法

介绍了进排气系统一维非定常流动的 FVM- TVD法 (总残差减少的有限体积法 )。控制方程的离散采用有限体积法 ,数值通量采用考虑截面变化的二阶 TVD格式。用该方法对 MIXPC系统排气管内的压力波进行了模拟 ,并与实测值对比 ,两者吻合较好 。

自适应Snake-TV模型在彩色图像修复中的应用

文章在整体变分模型(TV)的基础上引入蛇(Snake)模型,同时考虑待修复区域的形状,提出了自适应Snake-TV模型,并将其应用到灰度图像和彩色图像的修复,该模型能很好地保持图像的视觉连通性。试验结果表明:该算法在迭代次数相同的条件下修复彩色图像的效果比TV模型好得多;与Snake-CDD模型相比,修复效果更好,同时所用时间也大大减少。