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 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.

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.

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.

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。

加权核范数最小化和改进小波阈值函数的图像去噪算法

针对加权核范数最小化算法存在结构残余噪声以及无法较好地保持图像边缘结构的问题,提出基于加权核范数最小化和改进小波阈值函数的图像去噪算法。利用全变分模型对噪声图像进行初步去噪,使用噪声图像与初步去噪后的图像进行差分运算,对差分后得到的噪声残差图像使用改进的小波阈值函数去噪,将小波去噪后的残差图像与初步去噪图像叠加,将叠加后的图像使用基于残余噪声水平迭代的加权核范数最小化算法进行二次去噪。相较于当下主流去噪算法,经该算法处理后的图像的PSNR和SSIM值均有所提升,能够更好地保持图像的纹理结构,且在高噪声环境下效果更佳。

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。

张量分解和自适应图全变分的高光谱图像去噪

高光谱图像在采集过程中受到观测条件、成像仪材料属性、传输条件等客观因素的影响,不可避免地会引入各种噪声。这严重降低了高光谱图像的质量以及限制了后续处理的精度。因此,高光谱图像去噪是一个极其重要的预处理步骤。针对高光谱图像去噪问题,提出了低秩张量分解和自适应图全变分的高光谱图像去噪算法。首先,利用低秩张量分解来描述高光谱图像的全局空间和光谱相关性,并使用自适应权重图全变分来刻画高光谱图像空间维度上的分段平滑特性和保留高光谱图像的边缘信息;此外,采用l1-范数、Frobenius-范数分别刻画包括条纹噪声、脉冲噪声、死线噪声在内的稀疏噪声和高斯噪声。由此高光谱图像去噪问题归结为一个包含低秩张量分解和自适应图全变分的约束优化问题。利用增广拉格朗日乘子法对该优化问题进行交替求解。实验结果表明,所提出的高光谱图像去噪算法与现有的算法相比,能够充分刻画高光谱图像数据的内在结构特性,具有更好的去噪性能。

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。

改进后Ray-Box Intersection权系数矩阵的SART＋TVM重建算法

针对均匀采样稀疏型重建问题,研究了Ray-Box Intersection和传统的双线性插值两种权系数矩阵计算方法。对Ray-Box Intersection算法的逻辑漏洞进行了改进,利用直线生成法并结合射线斜率和像素位置关系提出了改进算法。该算法利用代数关系求得射线穿过重建区域的起点和终点,然后取不大于其坐标的最大整数,最后采用直线生成法,依据射线斜率沿X或Y方向得到该条射线在投影区域内经过的所有像素块的左下角坐标集合,对于每个像素块,射线与像素块交点距离即为该像素块对射线的加权值。实验结果表明,相比Ray-Box Intersection权系数矩阵算法,改进后算法速度提高了96%,最大峰值信噪比提高了19%;相比双线性插值算法,改进后算法收敛速度更快,在同样时间代价下最大峰值信噪比提高了5%。改进后的Ray-Box Intersection算法能够获得更好的重建图像质量,细节更清晰。

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。

一种改进的全变分(TV)修补模型

目的提出一种全变分(TV)修补模型的改进方案,而且具有良好的边缘特性,并弥补原有的TV修补模型的不能满足连通性原则的缺陷。方法定义在图像修补区定义一种"加权全变分,即沿修补区边界法线方向的梯度分量对全变分的贡献,远大于沿切线方向的梯度分量的贡献。结果通过对相同的受损图像,采用原有模型和改进模型作比对实验表明,文中的改进模型可以完全满足图像修补的连通性原则。结论改进的TV模型较原始TV模型更适合于非纹理图像修复。

DART-TV:高精度离散断层图像重建算法

本文提出了一种基于离散代数迭代(DART)和全变差(TV)最小化的高精度图像重建算法,即DART-TV。该算法适用于工业CT辅助制造中对于单一材料物体的尺寸检测,特别是在物体对某些角度无法被穿透的情况。传统的CT重建算法如FBP或ART无法有效处理这类数据缺失的病态问题,重建图像分辨率差。本文提出的算法的核心思想是:(1)利用DART的结果限制重建物体的支撑域,改善问题的病态性;(2)迭代过程中施加TV约束,以抑制噪声;(3)每次迭代后自动更新支撑域,不断逼近真实解,使得图像边缘更加清晰。初步的模拟结果和实验结果显示,DART-TV算法在有限角情况下可以得到更精确的重建图像边缘,并且该算法对于投影噪声和灰度值估计误差有更好的鲁棒性,可以应用于实际工程。

去除阶梯效应的TV+H^1+H^0变分分解模型

针对基于全变分(Total Variation,TV)极小的变分分解中,结构分量中容易出现阶梯现象而降低图像视觉效果的缺点,提出了一个去除阶梯效应的TV+H1+H0变分分解模型。新模型分别采用TV刻画结构分量的分片常值,采用H1半范数刻画分片光滑,则图像结构被看成是TV分量与H1分量之和。由于新的结构中包含了分片光滑的H1分量,所以可以一定程度去除阶梯现象。理论证明了模型的解的非平凡性,并且采用交替迭代算法对模型进行了数值求解。实验中以噪声人造图像和自然图像为实验对象,将分解模型应用到图像去噪,相对于经典的ROF模型和PVD模型,新模型取得了明显的优势。