A new two-step variational model for multiplicative noise removal with applications to texture images

Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motivated by the TV-Stokes model,we propose a new two-step variational model to denoise the texture images corrupted by multiplicative noise with a good geometry explanation in this paper.In the first step,we convert the multiplicative denoising problem into an additive one by the logarithm transform and propagate the isophote directions in the tangential field smoothing.Once the isophote directions are constructed,an image is restored to fit the constructed directions in the second step.The existence and uniqueness of the solution to the variational problems are proved.In these two steps,we use the gradient descent method and construct finite difference schemes to solve the problems.Especially,the augmented Lagrangian method and the fast Fourier transform are adopted to accelerate the calculation.Experimental results show that the proposed model can remove the multiplicative noise efficiently and protect the texture well.

Basilic vein variation encountered during surgery for arm vein port:A case report

BACKGROUND Venous variations are uncommon and usually hard to identify,and basilic vein variation is particularly rare.Basilic vein variation usually presents without any clinical symptoms and is often regarded as a benign alteration.This case was a patient with congenital basilic vein variation encountered during surgery for an infusion port.CASE SUMMARY We documented and analyzed an uncommon anatomical variation in the basilic vein encountered during arm port insertion.This peculiarity has hitherto remained undescribed in the literature.We offer remedial strategies for addressing this anomaly in the future and precautionary measures to circumvent its occurrence.We conducted a comprehensive review of analogous cases in the literature,offering pertinent therapeutic recommendations and solutions,with the aim of enhancing the efficacy and safety of future arm port implantations.CONCLUSION Venous variation is rare and requires detailed intraoperative and postoperative examination to ensure accuracy,so as not to affect subsequent treatment.

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.

Adaptive Reversible Visible Watermarking Based on Total Variation for BTC-Compressed Images

Few previous Reversible Visible Watermarking(RVW)schemes have both good transparency and watermark visibility.An adaptive RVW scheme that integrates Total Variation and visual perception in Block Truncation Coding(BTC)compressed domain,called TVB-RVW is proposed in this paper.A new mean image estimation method for BTC-compressed images is first developed with the help of Total Variation.Then,a visual perception factor computation model is devised by fusing texture and luminance characteristics.An adaptive watermark embedding strategy is used to embed the visible watermark with the effect of the visual perception factor in the BTC domain.Moreover,a lossless embedding method of the encrypted visible watermark is exploited to deter illegal watermark removal.The visible watermark can be removed since the visual perception factor and the estimated mean image remain unchanged before and after watermark embedding.Extensive experiments validate the superiority of the proposed algorithm over previous RVW schemes in BTC in terms of the visual quality of watermarked images and watermark visibility,and it can achieve a good balance between transparency and watermark visibility.

Total Electron Content Diurnal and Seasonal Variations and Response to Solar Events at Koudougou Station in Burkina Faso

In this paper, we studied the seasonal behavior of the total electron content (TEC) during a part of solar cycle 24 ascending, maximum and decreasing phases at Koudougou station (Latitude: 12°15'09"N Longitude: 2°21'45"W). Response of TEC to solar recurrent events is presented. The highest values of the TEC in 2014, 2015 and 2016 were recorded on March and October, while in 2013 they were recorded on April and November, corresponding to equinox months. This observation shows that TEC values at the equinoxes are higher than those of solstices. Moreover, the monthly TEC varies in phase with the sunspots number showing a linear dependence of the TEC on solar activity. The ionospheric electron contents are generally very low both before noon and during the night, but quite high at noon and after noon. This pattern of TEC variation is due to the fluctuation of incident solar radiation on the Earth’s equatorial ionosphere. During quiet periods, the number of free electrons generated is lower than that generated during recurrent periods, which shows a positive contribution of recurrent activity to the level of the TEC. Investigations have also highlighted a winter anomaly and equinoctial asymmetry in TEC behavior at Koudougou station.

Water Gauge Image Denoising Model Based on Improved Adaptive Total Variation

As an important part of water level warning in water conservancy projects,often due to the influence of environmental factors such as light and stains,the acquired water gauge images have sticky,broken and bright spot conditions,which affect the identification of water gauges.To solve this problem,a water gauge image denoising model based on improved adaptive total variation is proposed.Firstly,the regular term exponent in the adaptive total variational equation is changed to an inverse cosine function;secondly,the differential curvature is used to distinguish the image noise points and increase the smoothing strength at the noise points;finally,according to the characteristics of the gradient mode and adaptive gradient threshold after Gaussian filtering,the New model can adaptively denoise in the smooth area and protect the edge area,so as to have the characteristics of both edge-preserving denoising.The experimental results show that the new model has a great improvement in image vision,higher iteration efficiency and an average increase of 1.6 dB in peak signal-to-noise ratio,and an average increase of 9%in structural similarity,which is more beneficial to practical applications.

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.

Fast alternating direction method of multipliers for total-variation-based image restoration

A novel algorithm, i.e. the fast alternating direction method of multipliers （ADMM）, is applied to solve the classical total-variation （ TV ）-based model for image reconstruction. First, the TV-based model is reformulated as a linear equality constrained problem where the objective function is separable. Then, by introducing the augmented Lagrangian function, the two variables are alternatively minimized by the Gauss-Seidel idea. Finally, the dual variable is updated. Because the approach makes full use of the special structure of the problem and decomposes the original problem into several low-dimensional sub-problems, the per iteration computational complexity of the approach is dominated by two fast Fourier transforms. Elementary experimental results indicate that the proposed approach is more stable and efficient compared with some state-of-the-art algorithms.

图像重建中total variation正则化项的有限元计算方法

为在迭代图像重建算法中获得更高质量的重建图像,推导出TV(Total Variation)正则化项关于重建图像的Fréchet导数,并给出该导数的有限元表示;利用两个数值实验,分别采用不同的网格尺寸和不同的形函数验证该有限元表示结果.数值实验结果表明:采用相同的k次单纯形元时,随着网格不断加密,计算结果的L1和L2误差均下降;采用相同的网格时,线性单纯形元函数计算结果明显优于分片常数有限元和二次单纯形元计算结果.

Warm-Season Diurnal Variations of Total, Stratiform, Convective, and Extreme Hourly Precipitation over Central and Eastern China

Diurnal variations in amount, frequency and intensity of warm-season hourly precipitation(HP) at seven levels, which are defined as HP 0.1, 0.5, 1, 5, 10, 20 and 50 mm, are revealed based on no less than 30 years of hourly rain-gauge observations at national stations over central and eastern China(CEC). This study investigates the variations, relationships, differences and similarities of total, stratiform, convective and extreme HP over the entire CEC and various subregions. Results indicate that the variations in the amount and frequency of HP at the seven levels over the entire CEC all display a bimodal feature. For various regions, the variations of total HP mostly feature two peaks, while convective HP mainly occurs in the late afternoon and determines the diurnal variation of total HP intensity. On the basis of the primary peak time periods of HP frequency at all levels over different subregions, the variations can be classified into three main categories: late-afternoon primary peak, nocturnal primary peak, and time-shifting primary peak. However, the variations over some coastal regions like the Liaodong Peninsula, the Shandong Peninsula, and the coastal regions of Guangdong, distinctly differ from those over their corresponding larger regions. Overall, the normalized diurnal variation amplitude of amount and frequency increases with the increasing HP intensity; convective precipitation can be represented by HP 10 mm; and the intensity of HP 50 mm is slightly larger during the nighttime than during the daytime over the entire CEC. In northern China, diurnal variation in HP 5 mm can represent well that in convective precipitation.

Variation Features of Total Atmospheric Ozone in Beijing and Kunming Based on Dobson and TOMS Data

About 20 years of Dobson and TOMS data are used to analyze the variation characteristics of total atmospheric ozone in Beijing (39.93°N, 116.40°E) and Kunming (25.02°N, 102.68°E). It is shown that: (1) the long-term change trends for 1979 (or 1980)–2000 period are −0.642 DU/year and −0.009 DU/year respectively in Beijing and Kunming, (2) there are strong intra-seasonal variations especially in wintertime, which are comparable to seasonal variations both in Beijing and Kunming, (3) the long-term trend deduced from shorter time period of record is significantly different from that for longer time period, both in Kunming and Beijing, (4) there are significant QBO signals both in Beijing (mid latitude) and Kunming (low latitude), (5) the inter-annual variations of atmospheric ozone in both stations are mainly composed of the long-term trend and QBO signals, and (6) our Dobson and TOMS measurements of total ozone are generally in good agreement.

Algorithm for reconstructing compressed sensing color imaging using the quaternion total variation

A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing ability of the color image. First, the color image is converted from RGB （red, green, blue） space to CMYK （cyan, magenta, yellow, black） space, which is assigned to a quaternion matrix. Meanwhile, the quaternion matrix is converted into the information of the phase and amplitude by the Euler form of the quatemion. Secondly, the phase and amplitude of the quatemion matrix are used as the smoothness constraints for the compressed sensing （CS） problem to make the reconstructing results more accurate. Finally, an iterative method based on gradient is used to solve the CS problem. Experimental results show that by considering the information of the phase and amplitude, the proposed method can achieve better performance than the existing method that treats the three components of the color image as independent parts.

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.

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.

Spatial and temporal variations in pH and total alkalinity at the beginning of the rainy season in the Changjiang Estuary, China

The results of field observation carried out in May 2003 were used to examine pH and total alkalinity behaviors in the Changjiang Estuary. It was showed that pH and total alkalinity took on clear spatial variations in values with the minima in the low salinity region. Like salinity, transect distributions of pH and total alkalinity （TA） in a downriver direction had a sharp gradient each. These gradients appeared in such a sequence that the TA gradient was earlier than salinity and pH gradients, and the salinity gradient was earlier than the pH gradient. These distribution characteristics seemed to be strongly influenced by the mixing process of freshwater and seawater, for both pH and total alkalinity had significant linear relationships with salinity and temperature. For pH, phytoplankton activities also had a significant impact upon its spatial distribution. During a period of 48 h, pH and total alkalinity changed within wide ranges for every layer of the two anchor stations, namely, Stas 13 and 20, which were located at the mixed water mass and seawater mass, respectively. For both Stas 13 and 20, pH and TA fluctuation of every layer could be very wide during a 4 h period. As a whole, the data of the two anchor stations showed that neither variations in salinity and temperature nor phytoplankton activities were the main factors strongly influencing the total alkalinity temporal variability on a small time scale. The data of Sta. 20 implied that both salinity variation and phytoplankton activities had a significant influence on pH temporal variability, but the same conclusion could not be drawn from the data of Sta. 13.

Image haze removal via multiscale fusion and total variation

In foggy weather, images of outdoor scene are usually characterized with poor visibility as well as faint color saturation. The degraded hazy images may have substantial negative impact on most computer vision systems. Thus image haze removal is of the practical significance in engineering. This paper proposes a fast and effective single image haze removal algorithm on the basis of the physics imaging model. To extract the global atmospheric light accurately, we exploit multiple prior rules underlying hazy images, and put forward a novel measurement to judge the likelihood that a pixel is regarded as the global atmospheric light. In addition, the rough transmission map is estimated through a multiscale fusion process based on the Laplace pyramid transform, and refined by a total variation model. Experimental results demonstrate the proposed method outperforms most of the state-of-the-art algorithms in terms of the dehazing quality, and achieves a trade-off between the computational efficiency and haze removal capability.

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.

Adaptive Parameter Selection for Total Variation Image Deconvolution

In this paper,we propose a discrepancy rule-based method to automatically choose the regularization parameters for total variation image restoration problems. The regularization parameters are adjusted dynamically in each iteration.Numerical results are shown to illustrate the performance of the proposed method.

Hybrid reconstruction algorithm for computed tomography based on diagonal total variation

Inspired by total variation(TV), this paper represents a new iterative algorithm based on diagonal total variation(DTV) to address the computed tomography image reconstruction problem. To improve the quality of a reconstructed image, we used DTV to sparsely represent images when iterative convergence of the reconstructed algorithm with TV-constraint had no effect during the reconstruction process. To investigate our proposed algorithm, the numerical and experimental studies were performed, and rootmean-square error(RMSE) and structure similarity(SSIM)were used to evaluate the reconstructed image quality. The results demonstrated that the proposed method could effectively reduce noise, suppress artifacts, and reconstruct highquality image from incomplete projection data.