A new nonconvex approach for image restoration with Gamma noise

In this paper, a new total generalized variational(TGV) model for restoring images with multiplicative noise is proposed, which contains a nonconvex fidelity term and a TGV term. We use a difference of convex functions algorithm (DCA) to deal with the proposed model. For multiplicative noise removal, there exist many models and algorithms, most of which focus on convex approximation so that numerical algorithms with guaranteed convergence can be designed. Unlike these algorithms, we use the DCA algorithm to remove multiplicative noise. By numerical experiments, it is shown that the proposed approach leads to a better solution compared with the gradient projection algorithm for solving the classic multiplicative noise removal models. We prove that the sequence generated by the DCA algorithm converges to a stationary point, which satisfies the first order optimality condition. Finally, we demonstrate the performance of our whole scheme by numerical examples. A comparison with other methods is provided as well. Numerical results demonstrate that the proposed algorithm significantly outperforms some previous methods for multiplicative Gamma noise removal.     

保持泊松噪声图像细节的快速变分去噪算法

去除医学、天文图像中的泊松噪声一直是人们关注的热点问题之一。在充分分析泊松去噪[α]-Le模型的基础上结合交替方向乘子（ADMM）算法,给出该模型一基于框式约束的快速求解算法,并证明了该算法的收敛性。数值实验结果表明,该算法在去噪的同时,不仅能很好地保留图像中的边缘及小细节特征,还能大幅提高运算效率。     

Fast iterative algorithms for solving the minimization of curvature-related functionals in surface fairing

A number of successful variational models for processing planar images have recently been generalized to three-dimensional (3D) surface processing. With this new dimensionality, the amount of numerical computations to solve the minimization of such new 3D formulations naturally grows up dramatically. Though the need of computationally fast and efficient numerical algorithms able to process high resolution surfaces is high, much less work has been done in this area. Recently, a two-step algorithm for the fast solution of the total curvature model was introduced in Tasdizen, Whitaker, Burchard and Osher [Geometric surface processing via normal maps, ACM Trans. Graph. 22(4) (2003), pp. 1012–1033]. In this paper, we generalize and modify this algorithm to the solution of analogues of the mean curvature model of Droske and Martin Rumpf [A level set formulation for Willmore flow, Interfaces Free Bound. 6(3) (2004), pp. 361–378] and the Gaussian curvature model of Elsey and Esedo?lu [Analogue of the total variation denoising model in the context of geometry processing, Multiscale Model. Simul. 7(4) (2009), pp. 1549–1573]. Numerical experiments are shown to illustrate the good performance of the algorithms and test results.     

A fast minimization method for blur and multiplicative noise removal

Multiplicative noise and blur removal problems have attracted much attention in recent years. In this paper, we propose an efficient minimization method to recover images from input blurred and multiplicative noisy images. In the proposed algorithm, we make use of the logarithm to transform blurring and multiplicative noise problems into additive image degradation problems, and then employ l ₁-norm to measure in the data-fitting term and the total variation to measure the regularization term. The alternating direction method of multipliers (ADMM) is used to solve the corresponding minimization problem. In order to guarantee the convergence of the ADMM algorithm, we approximate the associated nonconvex domain of the minimization problem by a convex domain. Experimental results are given to demonstrate that the proposed algorithm performs better than the other existing methods in terms of speed and peak signal noise ratio.     

侧扫声呐图像的3维块匹配降斑方法

斑点噪声是影响侧扫声呐图像质量的主要因素,降斑处理对侧扫声呐图像的判别与分析非常重要。针对侧扫声呐图像自身特性和斑点噪声分布特点,提出一种基于3维块匹配(BM3D)的降斑方法。根据海底散射模型,得到侧扫声呐图像斑点噪声的瑞利分布模型,然后通过高斯光滑函数幂变换将瑞利分布的噪声转化为高斯分布,通过对数变换将乘性噪声转变为加性噪声,再进行自适应的BM3D滤波,最后采用逆变换得到降斑图像。实验结果表明,该方法在降噪、边缘和纹理保持等方面均优于空间域、小波域、Curvelet域的一些降斑方法。     

A Variational Model to Remove the Multiplicative Noise in Ultrasound Images

In this paper we study a variational model to deal with the speckle noise in ultrasound images. We prove the existence and uniqueness of the minimizer for the variational problem, and derive the existence and uniqueness of weak solutions for the associated evolution equation. Furthermore, we show that the solution of the evolution equation converges weakly in BV and strongly in L ² to the minimizer as t→∞. Finally, some numerical results illustrate the effectiveness of the proposed model for multiplicative noise removal.     

Numerical approximation of multiplicative SPDEs

We prove convergence of the stochastic exponential time differencing scheme for parabolic stochastic partial differential equations (SPDEs) with one-dimensional multiplicative noise. We examine convergence for fourth-order SPDEs and consider as an example the Swift–Hohenberg equation. After examining convergence, we present preliminary evidence of a shift in the deterministic pinning region [J. Burke and E. Knobloch, Localized states in the generalized Swift–Hohenberg equation, Phys. Rev. E. 73 (2006), pp. 056211-1–15; J. Burke and E. Knobloch, Snakes and ladders: Localized states in the Swift–Hohenberg equation, Phys. Lett. A 360 (2007), pp. 681–688; Y.-P. Ma, J. Burke, and E. Knobloch, Snaking of radial solutions of the multi-dimensional Swift–Hohenberg equation: A numerical study, Physica D 239 (2010), pp. 1867–1883; S. McCalla and B. Sandstede, Snaking of radial solutions of the multi-dimensional Swift–Hohenberg equation: A numerical study, Physica D 239 (2010), pp. 1581–1592] with space–time white noise.     

Multiplicative noise removal via combining total variation and wavelet frame

Total variation (TV) regularization has been proved effective for cartoon images restoration however it produces staircase effects, and properly wavelet frames were confirmed to provide a more smoothing approximation to the original image. In this paper, a new model for multiplicative noise removal was proposed, which combines wavelet frame-based regularization and TV regularization. A modified proximal linearized alternating direction method is developed to solve the proposed model, considering that adding a new regularization term to the TV model would yield more parameters, which will result in computational difficulties. For the new model, the existence of solution and the convergence property of the proposed algorithm are proved. Numerical experiments have proved that the proposed model has a superior performance in terms of the peak signal-to-noise ratio and the relative error values for non-piecewise constant images when compared with some state-of-the-art multiplicative noise removal models.     

A two-stage method for oil slick segmentation

In this paper we propose a two-stage algorithm for oil slick segmentation in synthetic aperture radar (SAR) images. In the first stage, we propose a new variational model to reduce speckles in non-textured SAR images. Applications to simulated and real SAR images show that the method is well balanced in the quality of the conventional criteria. Then, in the second stage, we use the fast Chan–Vese (CV) model and the level set method to segment the oil slick in the de-speckled SAR image. The additive operator splitting (AOS) scheme is used in the numerical implementation to improve computational efficiency. Experimental results show that our two-stage algorithm is effective for oil slick segmentation in SAR images.     

彩色图像乘性噪声去除的高阶变分模型

传统的变分去噪模型中,MTV模型去噪后的图像可以较好的保持图像的边缘,但会有阶梯效应。高阶TC模型可以防止阶梯效应,但是边缘保持不好。采用耦合的MTV模型和高阶TC模型相结合的方法,构造出新的混合模型,并推广到彩色图像乘性噪声去除的高阶变分模型。为提高新模型的计算效率,引入辅助变量和拉格朗日乘子设计了相应的增广拉格朗日算法。实验结果表明,新模型在处理彩色图像时能有效地避免阶梯效应,同时保持图像的边缘和细节。与实验中的传统模型相比,新模型的峰值信噪比和结构相似性指数均有提升。     

色关联的色噪声驱动的双稳杜芬模型的稳态分析

研究了由色关联的色噪声驱动的双稳杜芬模型的稳态概率密度函数及状态变量的均值和标准方差．首先应用一致有色噪声近似方法,推导出了具有色关联的色噪声驱动的双稳杜芬模型的稳态概率密度函数的解析表达式．分析了噪声的“有色性”及关联性对稳态密度函数和状态变量的均值、标准方差的影响,发现了一些由白噪声激励的杜芬模型中不会出现的新的非线性现象：加性噪声强度、噪声之间的关联系数和关联时间都能够诱导非平衡相变．     

一种新的去除混合噪声的变分模型及其应用

由于现有的某些去噪模型仅对某种噪声特别有效,而对其它类型噪声的效果却不够显著,因此提出一种能有效地去除多种噪声的变分模型,它融合了几种经典去噪模型的优点,并在数值求解时采用了高效且无条件稳定的AOS算法.数值实验表明,与现有的一些去噪方法相比,提出的去噪方法耗时少且效果更好.最后给出了解的存在性证明.     

Image restoration based on camera microscanning

Common restoration techniques use a single observed image for the processing. In this work three observed degraded images obtained from camera microscanning are utilized for image restoration. It is assumed that the degraded images contain information about an original image, multiplicative interference, and additive sensor’s noise. Using captured images a set of linear or nonlinear equations and objective function are formed. By solving the system of equations with the help of an iterative algorithm, the original image can be recovered. A fast algorithm for approximated image restoration is proposed. Computer simulations results presented and discussed.     

Using reproducing kernel for solving a class of singular weakly nonlinear boundary value problems

Cui et al. [M. Cui and F. Geng, Solving singular two point boundary value problems in reproducing kernel space, J. Comput. Appl. Math. 205 (2007), pp. 6–15; H. Yao and M. Cui, A new algorithm for a class of singular boundary value problems, Appl. Math. Comput. 186 (2007), pp. 1183–1191] presents an algorithm to solve a class of singular linear boundary value problems in the reproducing kernel space. In this paper, we will present three new algorithms to solve a class of singular weakly nonlinear boundary value problems in reproducing kernel space. The algorithms are efficiently applied to solving some model problems. It is demonstrated by the numerical examples that those algorithms are highly accurate.     

A fast numerical algorithm for solving nearly penta-diagonal linear systems

In this paper, we present a fast numerical algorithm for solving nearly penta-diagonal linear systems and show that the computational cost is less than those of three algorithms in El-Mikkawy and Rahmo, [Symbolic algorithm for inverting cyclic penta-diagonal matrices recursively–Derivation and implementation, Comput. Math. Appl. 59 (2010), pp. 1386–1396], Lv and Le [A note on solving nearly penta-diagonal linear systems, Appl. Math. Comput. 204 (2008), pp. 707–712] and Neossi Nguetchue and Abelman [A computational algorithm for solving nearly penta-diagonal linear systems, Appl. Math. Comput. 203 (2008), pp. 629–634.]. In addition, an efficient way of evaluating the determinant of a nearly penta-diagonal matrix is also discussed. The algorithm is suited for implementation using computer algebra systems (CAS) such as MATLAB, MACSYMA and MAPLE. Some numerical examples are given in order to illustrate the efficiency of our algorithm.     

Adaptive Variational Method for Restoring Color Images with High Density Impulse Noise

In this paper, a new variational framework of restoring color images with impulse noise is presented. The novelty of this work is the introduction of an adaptively weighting data-fidelity term in the cost functional. The fidelity term is derived from statistical methods and contains two weighting functions as well as some statistical control parameters of noise. This method is based on the fact that impulse noise can be approximated as an additive noise with probability density function (PDF) being the finite mixture model. A Bayesian framework is then formulated in which likelihood functions are given by the mixture model. Inspired by the expectation-maximization (EM) algorithm, we present two models with variational framework in this study. The superiority of the proposed models is that: the weighting functions can effectively detect the noise in the image; with the noise information, the proposed algorithm can automatically balance the regularity of the restored image and the fidelity term by updating the weighting functions and the control parameters. These two steps ensure that one can obtain a good restoration even though the degraded color image is contaminated by impulse noise with large ration (90% or more). In addition, the numerical implementation of this algorithm is very fast by using a split algorithm. Some numerical experimental results and comparisons with other methods are provided to show the significant effectiveness of our approach.     

基于偏微分方程的变分去噪模型

针对现有去除图像乘性噪声的变分模型的保真项中存在病态条件的问题,结合全变分方法和对数变换的相关理论对保真项进行分析,提出一种新的基于偏微分方程(PDE)的去除图像乘性噪声的变分模型,导出了该模型对应的偏微分方程初边值问题,并给出了相应的数值计算方法。从数值实验结果可以看出,所提模型的均方误差(MSE)明显下降,峰值信噪比(PSNR)明显提升,同时很好地避免了模型的病态情形,对去除图像乘性噪声的变分模型中保真项存在的病态条件提供了很好的解决办法,减小了离散化过程中可能存在的误差。数值实验结果表明,所提模型具有良好的去噪效果,能够较好地抑制图像中的“阶梯效应”现象。     

The Bruck's ergodic iteration method for the Ky Fan inequality over the fixed point set

We introduce a new iteration algorithm for solving the Ky Fan inequality over the fixed point set of a nonexpansive mapping, where the cost bifunction is monotone without Lipschitz-type continuity. The algorithm is based on the idea of the ergodic iteration method for solving multi-valued variational inequality which is proposed by Bruck [On the weak convergence of an ergodic iteration for the solution of variational inequalities for monotone operators in Hilbert space, J. Math. Anal. Appl. 61 (1977), pp. 159–164] and the auxiliary problem principle for equilibrium problems P.N. Anh, T.N. Hai, and P.M. Tuan. [On ergodic algorithms for equilibrium problems, J. Glob. Optim. 64 (2016), pp. 179–195]. By choosing suitable regularization parameters, we also present the convergence analysis in detail for the algorithm and give some illustrative examples.     

一类非线性网络系统中噪声改善信号的相关性

基于相关系数研究了在一类非线性神经网络系统中加性和乘性噪声作用下的阈上随机共振现象。仅在加性噪声或者乘性噪声的作用下,对每一个固定的系统阈值,加性噪声下的阈上随机共振比乘性噪声下的阈上随机共振更容易发生,且相关系数所达到的峰值也比在乘性噪声下的峰值大,这说明加性噪声更有利于改善信号的相关性。系统阈值的增加会降低阈上随机共振的功效;而阈值单元数目的增多,会提高阈上随机共振的功效。加性和乘性噪声共同作用下的阈上随机共振现象同样存在,对系统阈值进行恰当选取和增加系统阈值单元数目使得阈上随机共振现象更加明显;给定乘性噪声而改变加性噪声比固定加性噪声而改变乘性噪声阈上随机共振更容易发生,且功效更好。     

A well-balanced and adaptive variational model for the removal of mixed noise

Image denoising is one of the fundamental problems concerning image processing. Over the last decade mathematical models based on partial differential equations and variational techniques have led to superior results related to denoising problems. The additive noise models have been studied extensively, however, the reconstruction of images corrupted by nonadditive noise has not yet been thoroughly studied. In this paper, a novel variational method for the reconstruction of images corrupted by non-uniformly distributed noise is presented. The proposed model includes a balance between the data term and the regularization term in the energy functional, which takes into account the statistical control of the parameters and the position of the noisy points related to the edges presented in the image. The parameters are determined by the given initial noisy image. The obtained results have shown the effectiveness and robustness of the proposed model and in restoring images with multiplicative noise or mixed Gaussian noise, while preserving edges and small structures belonging to the image.