Image restoration with a high-order total variation minimization method

In this paper, we propose a fast and efficient way to restore blurred and noisy images with a high-order total variation minimization technique. The proposed method is based on an alternating technique for image deblurring and denoising. It starts by finding an approximate image using a Tikhonov regularization method. This corresponds to a deblurring process with possible artifacts and noise remaining. In the denoising step, a high-order total variation algorithm is used to remove noise in the deblurred image. We see that the edges in the restored image can be preserved quite well and the staircase effect is reduced effectively in the proposed algorithm. We also discuss the convergence of the proposed regularization method. Some numerical results show that the proposed method gives restored images of higher quality than some existing total variation restoration methods such as the fast TV method and the modified TV method with the lagged diffusivity fixed-point iteration.     

利用对偶树复数小波与全变差模型实现图像去噪的新方法

本文首先研究了一种三层小波系数相关萎缩的概念与性质,利用对偶树复数小波与全变差模型相结合,提出了一种新的图像去噪方法。实验结果表明,与现有的图像去噪方法相比,本文方法无论是在视觉还是在均方误差等方面均有更好的效果。     

Compression and denoising using <Emphasis Type="Italic">l</Emphasis><Subscript>0</Subscript>-norm

In this paper, we deal with l ₀-norm data fitting and total variation regularization for image compression and denoising. The l ₀-norm data fitting is used for measuring the number of non-zero wavelet coefficients to be employed to represent an image. The regularization term given by the total variation is to recover image edges. Due to intensive numerical computation of using l ₀-norm, it is usually approximated by other functions such as the l ₁-norm in many image processing applications. The main goal of this paper is to develop a fast and effective algorithm to solve the l ₀-norm data fitting and total variation minimization problem. Our idea is to apply an alternating minimization technique to solve this problem, and employ a graph-cuts algorithm to solve the subproblem related to the total variation minimization. Numerical examples in image compression and denoising are given to demonstrate the effectiveness of the proposed algorithm.     

Generalizing the ENO-DB2<Emphasis Type="Italic">p</Emphasis> transform using the inverse wavelet transform

The essentially non-oscillatory (ENO)-wavelet transform developed by Chan and Zhou (SIAM J. Numer. Anal. 40(4), 1369–1404, 2002) is based on a combination of the Daubechies-2p wavelet transform and the ENO technique. It uses extrapolation methods to compute the scaling coefficients without differencing function values across jumps and obtains a multiresolution framework (essentially) free of edge artifacts. In this work, we present a different way to compute the ENO-DB2p wavelet transform of Chan and Zhou which allows us to simplify the process and easily generalize it to other families of orthonormal wavelets.     

Feedback flow control employing local dynamical modelling with wavelets

In this paper, we utilize wavelet transform to obtain dynamical models describing the behaviour of fluid flow in a local spatial region of interest. First, snapshots of the flow are obtained from experiments or from computational fluid dynamics (CFD) simulations of the governing equations. A wavelet family and decomposition level is selected by assessing the reconstruction success under the resulting inverse transform. The flow is then expanded onto a set of basis vectors that are constructed from the wavelet function. The wavelet coefficients associated with the basis vectors capture the time variation of the flow within the spatial region covered by the support of the basis vectors. A dynamical model is established for these coefficients by using subspace identification methods. The approach developed is applied to a sample flow configuration on a square domain where the input affects the system through the boundary conditions. It is observed that there is good agreement between CFD simulation results and the predictions of the dynamical model. A controller is designed based on the dynamical model and is seen to be successful in regulating the velocity of a given point within the region of interest.     

基于Curvelet域高斯尺度混合模型的地震信号降噪方法

结合曲波变换和高斯尺度混合模型提出地震信号随机噪声压制方法.该方法首先运用曲波变换对含有随机噪声的地震信号进行分解,然后对各小波子带系数分别建立高斯尺度混合模型估计出原始地震信号所对应的小波系数,最后经曲波逆变换重构获得降噪处理后的地震信号.仿真地震信号和实际地震信号的实验结果均表明本文方法能够有效压制地震信号中的随机噪声干扰,较多地保留了有效信号.     

The Discrete Wavelet Transform in S

Abstract

The theory of wavelets has recently undergone a period of rapid development. We introduce a software package called wavethresh that works within the statistical language S to perform one- and two-dimensional discrete wavelet transforms. The transforms and their inverses can be computed using any particular wavelet selected from a range of different families of wavelets. Pictures can be drawn of any of the one- or two-dimensional wavelets available in the package. The wavelet coefficients can be presented in a variety of ways to aid in the interpretation of data. The package's wavelet transform “engine” is written in C for speed and the object-oriented functionality of S makes wavethresh easy to use. We provide a tutorial introduction to wavelets and the wavethresh software. We also discuss how the software may be used to carry out nonlinear regression and image compression. In particular, thresholding of wavelet coefficients is a method for attempting to extract signal from noise and wavethresh includes functions to perform thresholding according to methods in the literature.     

A wavelet-based spectral procedure for steady-state simulation analysis

We develop WASSP, a wavelet-based spectral method for steady-state simulation analysis. First WASSP determines a batch size and a warm-up period beyond which the computed batch means form an approximately stationary Gaussian process. Next WASSP computes the discrete wavelet transform of the bias-corrected log-smoothed-periodogram of the batch means, using a soft-thresholding scheme to denoise the estimated wavelet coefficients. Then taking the inverse discrete wavelet transform of the thresholded wavelet coefficients, WASSP computes estimators of the batch means log-spectrum and the steady-state variance parameter (i.e., the sum of covariances at all lags) for the original (unbatched) process. Finally by combining the latter estimator with the batch means grand average, WASSP provides a sequential procedure for constructing a confidence interval on the steady-state mean that satisfies user-specified requirements concerning absolute or relative precision as well as coverage probability. An experimental performance evaluation demonstrates WASSP’s effectiveness compared with other simulation analysis methods.     

A fast alternating minimization algorithm for total variation deblurring without boundary artifacts

Recently, a fast alternating minimization algorithm for total variation image deblurring (FTVd) has been presented by Wang, Yang, Yin, and Zhang (2008) [32]. The method in a nutshell consists of a discrete Fourier transform-based alternating minimization algorithm with periodic boundary conditions and in which two fast Fourier transforms (FFTs) are required per iteration. In this paper, we propose an alternating minimization algorithm for the continuous version of the total variation image deblurring problem. We establish convergence of the proposed continuous alternating minimization algorithm. The continuous setting is very useful to have a unifying representation of the algorithm, independently of the discrete approximation of the deconvolution problem, in particular concerning the strategies for dealing with boundary artifacts. Indeed, an accurate restoration of blurred and noisy images requires a proper treatment of the boundary. A discrete version of our continuous alternating minimization algorithm is obtained following two different strategies: the imposition of appropriate boundary conditions and the enlargement of the domain. The first one is computationally useful in the case of a symmetric blur, while the second one can be efficiently applied for a nonsymmetric blur. Numerical tests show that our algorithm generates higher quality images in comparable running times with respect to the Fast Total Variation deconvolution algorithm.     

波场逆向外推脊小波成像方法

地震偏移波动方程成像问题本质上讲是数学逆问题,传统方法求解采用的基函数具有明显的不足,针对其存在的不足,以及研究对象的地质构造特点,利用最新的具有较好光滑性、紧支性的Ridgelet函数基,以及能较好表征地质构造的平面或平面特征的数学分析工具;Ridgelet变换,提出了相应的改进方法,利用变换后系数的较好稀疏性,建立了多尺度脊小波波场递推成像计算方法。     

基于小波变换和矢量量化的人脸图像压缩

提出一种新的在小波域内进行矢量量化的算法.该算法在对图像进行多级小波变换后,构造三个方向的跨频带矢量,同时采用分类矢量量化,非线性插补矢量量化和基于人眼视觉特性的加权矢量量化,提高了图像的编码效率和重构质量.仿真结果表明,该算法实现简单,在较低的编码率下,可达到较好的压缩效果.     

Fusion of multi-spectral and panchromatic images using fuzzy rule

A fusion approach is proposed to refine the resolution of a multi-spectral image using a high-resolution panchromatic image. After the two images are decomposed by wavelet transform, five texture features are extracted from the high-frequency detailed sub-images. Then a nonlinear fusion rule, i.e. fuzzy rule is used to merge wavelet coefficients from the two images according to the extracted features. Experimental results indicate that the method outperforms the traditional approaches in preserving spectral information while improving spatial information.     

Sparse multiscale representation of Galerkin method for solving linear-mixed Volterra-Fredholm integral equations

This paper presents an efficient method for solving the linear-mixed Volterra-Fredholm equations using multiscale transformation. For this purpose, by changing the variables, the Fredholm-Volterra equation is discretized using wavelet Galerkin method. This equation reduces to a set of linear algebraic equations by using the wavelet transform matrix and the operational matrix of integration. To reach the sparse coefficients matrix for having a reduction in the computational cost, thresholding is used. This sparse system solves by generalized minimal residual (GMRES) method. If the appropriate threshold selects, the number of nonzero coefficients reduces while the error will not be less than a certain amount. The convergence analysis has been investigated. The validity and applicability of the technique are illustrated by a series of numerical tests.     

Smoothness Estimates for Soft-Threshold Denoising via Translation-Invariant Wavelet Transforms

In this paper, we study a generalization of the Donoho–Johnstone denoising model for the case of the translation-invariant wavelet transform. Instead of soft-thresholding coefficients of the classical orthogonal discrete wavelet transform, we study soft-thresholding of the coefficients of the translation-invariant discrete wavelet transform. This latter transform is not an orthogonal transformation. As a first step, we construct a level-dependent threshold to remove all the noise in the wavelet domain. Subsequently, we use the theory of interpolating wavelet transforms to characterize the smoothness of an estimated denoised function. Based on the fact that the inverse of the translation-invariant discrete transform includes averaging over all shifts, we use smoother autocorrelation functions in the representation of the estimated denoised function in place of Daubechies scaling functions.     

Wavelet Image Compressor—MinImage

Nowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. MinImage, the major topic of this paper, is an application that compresses still images by wavelets. MinImage is used to compress grayscale images and true color images. It implements the wavelet transform to code standard BMP image files to LET wavelet image files, which is defined in MinImage. The code is written in C++ on the Microsoft Windows NT platform. This paper illustrates the design and implementation details in Min-Image according to the image compression stages. First, the preprocessor generates the wavelet transform blocks. Second, the basic wavelet decomposition is applied to transform the image data to the wavelet coefficients. The discrete wavelet transforms are the kernel component of MinImage and are discussed in detail. The different wavelet transforms can be plugged in to extend the functionality of MinImage. The third step is the quantization. The standard scalar quantization algorithm and the optimized quantization algorithm, as well as the dequantization, are described. The last part of MinImage is the entropy-coding schema. The reordering of the coefficients based on the Peano Curve and the different entropy coding methods are discussed. This paper also gives the specification of the wavelet compression parameters adjusted by the end user. The interface, parameter specification, and analysis of MinImage are shown in the final appendix.     

Regularization parameter selection and an efficient algorithm for total variation-regularized positron emission tomography

In positron emission tomography, image data corresponds to measurements of emitted photons from a radioactive tracer in the subject. Such count data is typically modeled using a Poisson random variable, leading to the use of the negative-log Poisson likelihood fit-to-data function. Regularization is needed, however, in order to guarantee reconstructions with minimal artifacts. Given that tracer densities are primarily smoothly varying, but also contain sharp jumps (or edges), total variation regularization is a natural choice. However, the resulting computational problem is quite challenging. In this paper, we present an efficient computational method for this problem. Convergence of the method has been shown for quadratic regularization functions and here convergence is shown for total variation regularization. We also present three regularization parameter choice methods for use on total variation-regularized negative-log Poisson likelihood problems. We test the computational and regularization parameter selection methods on two synthetic data sets.     

小波尺度函数计算的广义高斯积分法及其应用

对于小波尺度函数变换的分解系数的积分运算建立了以尺度函数为权的广义高斯积分方法的运算格式．借助于样条函数,证明了其广义高斯积分随小波分解水平（resolutionlevel）指标的上升而收敛．在此基础上给出了以小波尺度函数变换重构或逼近任一函数的显式解析式,并对具有函数算子、微分或积分算子的运算给出了变换规则．这对于求解复杂非线性方程（组）是一种强有力的工具．最后给出了用该文方法求解非线性二点边值问题的算例．     

Wavelet optimized finite difference method using interpolating wavelets for self-adjoint singularly perturbed problems

We design a wavelet optimized finite difference (WOFD) scheme for solving self-adjoint singularly perturbed boundary value problems. The method is based on an interpolating wavelet transform using polynomial interpolation on dyadic grids. Small dissipation of the solution is captured significantly using an adaptive grid. The adaptive feature is performed automatically by thresholding the wavelet coefficients. Numerical examples have been solved and compared with non-standard finite difference schemes in [J.M.S. Lubuma, K.C. Patidar, Uniformly convergent non-standard finite difference methods for self-adjoint singular perturbation problems, J. Comput. Appl. Math. 191 (2006) 228–238]. The proposed method outperforms the non-standard finite difference for studying singular perturbation problems for small dissipations (very small ) and effective grid generation. Therefore, the proposed method is better for studying the more challenging cases of singularly perturbed problems.     

Anisotropic Total Variation Filtering

Total variation regularization and anisotropic filtering have been established as standard methods for image denoising because of their ability to detect and keep prominent edges in the data. Both methods, however, introduce artifacts: In the case of anisotropic filtering, the preservation of edges comes at the cost of the creation of additional structures out of noise; total variation regularization, on the other hand, suffers from the stair-casing effect, which leads to gradual contrast changes in homogeneous objects, especially near curved edges and corners. In order to circumvent these drawbacks, we propose to combine the two regularization techniques. To that end we replace the isotropic TV semi-norm by an anisotropic term that mirrors the directional structure of either the noisy original data or the smoothed image. We provide a detailed existence theory for our regularization method by using the concept of relaxation. The numerical examples concluding the paper show that the proposed introduction of an anisotropy to TV regularization indeed leads to improved denoising: the stair-casing effect is reduced while at the same time the creation of artifacts is suppressed.     

适应于图像压缩的11/9小波基构造

根据正交多分辨分析理论,利用求解低通和高通滤波的系数,可构造出多种正交小波.但正交小波中只有Haar小波满足对称性,这不适合在图像处理方面的应用.在提升格式的小波变换出现之前,小波分解通过Mallat算法来完成,而提升格式的小波有显著的优点,运算量少,不同小波运算量减少程度不一样,一般减少在25%到50%之间.文章根据双正交对称紧支集小波的消失矩、对称性、短支撑等一系列条件和其他构造原理,构造出一个适应图像压缩的11/9双正交提升小波,并满足Cohen-Daubechies准则.同时,为了便于小波变换的硬件实现,最佳的状态是,分解和重构滤波系数为二进制分数,且根据不同参数取值,让子带编码增益G_(SBC)达到最大.