A modified spectral conjugate gradient projection algorithm for total variation image restoration

In this study, a modified spectral conjugate gradient projection method is presented to solve total variation image restoration, which is transferred into the nonlinear constrained optimization with the closed constrained set. The global convergence of the proposed scheme is analyzed. In the end, some numerical results illustrate the efficiency of this method.     

A hybrid convex variational model for image restoration

We propose a new hybrid model for variational image restoration using an alternative diffusion switching non-quadratic function with a parameter. The parameter is chosen adaptively so as to minimize the smoothing near the edges and allow the diffusion to smooth away from the edges. This model belongs to a class of edge-preserving regularization methods proposed in the past, the ?-function formulation. This involves a minimizer to the associated energy functional. We study the existence and uniqueness of the energy functional of the model. Using real and synthetic images we show that the model is effective in image restoration.     

Split Bregman iteration algorithm for total bounded variation regularization based image deblurring

Many existing algorithms taking the seminorm in BV(Ω) for regularization have achieved great success in image processing. However, this paper considers the total bounded variation regularization based approach to perform image deblurring. Based on this novel model, we introduce an extended split Bregman iteration to obtain the optimum solution quickly. We also provide the rigorous convergence analysis of the iterative algorithm here. Compared with the results of the ROF method, numerical simulations illustrate the more excellent reconstruction performance of the proposed algorithm.     

On decomposition-based block preconditioned iterative methods for half-quadratic image restoration

Image restoration is a fundamental problem in image processing. Except for many different filters applied to obtain a restored image in image restoration, a degraded image can often be recovered efficiently by minimizing a cost function which consists of a data-fidelity term and a regularization term. In specific, half-quadratic regularization can effectively preserve image edges in the recovered images and a fixed-point iteration method is usually employed to solve the minimization problem. In this paper, the Newton method is applied to solve the half-quadratic regularization image restoration problem. And at each step of the Newton method, a structured linear system of a symmetric positive definite coefficient matrix arises. We design two different decomposition-based block preconditioning matrices by considering the special structure of the coefficient matrix and apply the preconditioned conjugate gradient method to solve this linear system. Theoretical analysis shows the eigenvector properties and the spectral bounds for the preconditioned matrices. The method used to analyze the spectral distribution of the preconditioned matrix and the correspondingly obtained spectral bounds are different from those in the literature. The experimental results also demonstrate that the decomposition-based block preconditioned conjugate gradient method is efficient for solving the half-quadratic regularization image restoration in terms of the numerical performance and image recovering quality.     

A PDE approach to image restoration problem with observation on a meager domain

We present here a new nonlinear PDE approach to image restoration (the inpainting problem) using a meager blurred image or a finite number of observation points. To this end, one uses a least square approach with the H⁻¹ distributional metric. Some important theoretical and numerical results are provided in this paper.     

Modular solvers for image restoration problems using the discrepancy principle

Many problems in image restoration can be formulated as either an unconstrained non‐linear minimization problem, usually with a Tikhonov‐like regularization, where the regularization parameter has to be determined; or as a fully constrained problem, where an estimate of the noise level, either the variance or the signal‐to‐noise ratio, is available. The formulations are mathematically equivalent. However, in practice, it is much easier to develop algorithms for the unconstrained problem, and not always obvious how to adapt such methods to solve the corresponding constrained problem. In this paper, we present a new method which can make use of any existing convergent method for the unconstrained problem to solve the constrained one. The new method is based on a Newton iteration applied to an extended system of non‐linear equations, which couples the constraint and the regularized problem, but it does not require knowledge of the Jacobian of the irregularity functional. The existing solver is only used as a black box solver, which for a fixed regularization parameter returns an improved solution to the unconstrained minimization problem given an initial guess. The new modular solver enables us to easily solve the constrained image restoration problem; the solver automatically identifies the regularization parameter, during the iterative solution process. We present some numerical results. The results indicate that even in the worst case the constrained solver requires only about twice as much work as the unconstrained one, and in some instances the constrained solver can be even faster. Copyright © 2002 John Wiley & Sons, Ltd.     

A fourth-order boundary value problem for a Sturm-Liouville type equation

An existence result of multiple solutions for a fourth-order Sturm-Liouville boundary value problem with variable parameters is established. As a consequence, three solutions for a boundary value problem with a fourth-order equation in a complete form are obtained. Our approach is based on variational methods.     

THE SIMPLE BLOCK ITERATIVE ALGORITHM OF AN EXTENDED ENTROPY MODEL AND ITS CONVERGENCE

1.ExtendedEntropyModelEntropymodelsarewidelyusedasaforecastingtechniqueinregionalplanning,see11]and[7].Theydescribeinacertainwaythemostprobablespatialinteractionandcanbeusedtoestimateorigin-destinationtripmatrices.Butthemodelsaretoosimple,soitisdifficulttomeetpracticalrequirements.HallefjordandJo..stenl2]putforwardthegeneralizationofthestandardentropymodels.Onthebasisofthisandaccordingtothedemandofthepredictionofthetripamountfortransportationplanning,wearriveatthefollowing:wherep,μandnarepo…     

A Nash-game approach to joint image restoration and segmentation

We propose a game-theoretic approach to simultaneously restore and segment noisy images. We define two players: one is restoration, with the image intensity as strategy, and the other is segmentation with contours as strategy. Cost functions are the classical relevant ones for restoration and segmentation, respectively. The two players play a static game with complete information, and we consider as solution to the game the so-called Nash equilibrium. For the computation of this equilibrium we present an iterative method with relaxation. The results of numerical experiments performed on some real images show the relevance and efficiency of the proposed algorithm.     

A new trust region algorithm for image restoration

The image restoration problems play an important role in remote sensing and astronomical image analysis. One common method for the recovery of a true image from corrupted or blurred image is the least squares error (LSE) method. But the LSE method is unstable in practical applications. A popular way to overcome instability is the Tikhonov regularization. However, difficulties will encounter when adjusting the so-called regularization parameter a. Moreover, how to truncate the iteration at appropriate steps is also challenging. In this paper we use the trust region method to deal with the image restoration problem, meanwhile, the trust region subproblem is solved by the truncated Lanczos method and the preconditioned truncated Lanczos method. We also develop a fast algorithm for evaluating the Kronecker matrix-vector product when the matrix is banded. The trust region method is very stable and robust, and it has the nice property of updating the trust region automatically. This releases us from tedious fi     

A special Hermitian and skew-Hermitian splitting method for image restoration

In this paper, we consider an ill-posed image restoration problem with a noise contaminated observation, and a known convolution kernel. A special Hermitian and skew-Hermitian splitting (HSS) iterative method is established for solving the linear systems from image restoration. Our approach is based on an augmented system formulation. The convergence and operation cost of the special HSS iterative method for image restoration problems are discussed. The optimal parameter minimizing the spectral radius of the iteration matrix is derived. We present a detailed algorithm for image restoration problems. Numerical examples are given to demonstrate the performance of the presented method. Finally, the SOR acceleration scheme for the special HSS iterative method is discussed.     

Bregman iterative model using the G-norm

In this paper, we analyze the Bregman iterative model using the G-norm. Firstly, we show the convergence of the iterative model. Secondly, using the source condition and the symmetric Bregman distance, we consider the error estimations between the iterates and the exact image both in the case of clean and noisy data. The results show that the Bregman iterative model using the G-norm has the similar good properties as the Bregman iterative model using the L2-norm.     

An edge driven wavelet frame model for image restoration

Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for image restoration by approximating images as piecewise smooth functions. With an implicit representation of image singularities sets, the proposed model inflicts different strength of regularization on smooth and singular image regions and edges. The proposed edge driven model is robust to both image approximation and singularity estimation. The implicit formulation also enables an asymptotic analysis of the proposed models and a rigorous connection between the discrete model and a general continuous variational model. Finally, numerical results on image inpainting and deblurring show that the proposed model is compared favorably against several popular image restoration models.     

A hybrid projective method for solving system of equilibrium problems with demicontractive mappings applicable in image restoration problems

In this paper, we propose a general iterative scheme based on CQ projection method for finding a common solution of system of equilibrium problems and the fixed point set of a finite family of demicontractive mappings. We also prove strong convergence of the scheme to a common element of the two above-described sets. We then give a numerical example to justify our main result. An example is given in an infinite dimensional space for supporting our main result. Moreover, we apply our main result to solve the unconstrained image restoration problems with a finite family of blurring operators. Our results extend and improve some existing results in the literature.     

New time dependent pretreat models based on total variational image restoration

In this paper,we propose new pretreat models for total variation (TV) minimization problems in image deblurring and denoising.Specially,blur operator is considered as useful information in restoration.New models in form is equivalent to pretreat the initial value by image blur operator.We successfully get a new (L.Rudin,S.Osher,and E.Fatemi) ROF model,a new level set motion model and a new anisotropic diffusion model respectively.Numerical experiments demonstrate that,under the same stopping rule,the proposed methods significantly accelerate the convergence of the mothed,save computation time and get the same restored effect.     

A new nonlocal model for the restoration of textured images

In this paper, we focus on the mathematical and numerical study of a new nonlocal reaction-diffusion system for image denoising. This model is motivated by involving the decomposition approach of $H^{-1}$ norm suggested by Meyer [25] which is more appropriate to represent the oscillatory patterns and small details in the textured image. Based on Schaeffer''s fixed point theorem, we prove the existence and uniqueness of solution of the proposed model. To illustrate the efficiency and effectiveness of our model, we test the denoising experimental results as well we compare with some existing models in the literature.     

Split Bregman method for the modified lot model in image denoising

In this paper a split Bregman iteration is proposed for the modified LOT model in image denoising. We first use the split Bregman method to solve the ROF model which can be seen as an approximate form of the first step of the original LOT model. Then we use a modified split Bregman method to fit the second step of the LOT model and give the convergence of the proposed split Bregman method. Several numerical examples are arranged to show the effectiveness of the proposed method.     

Convergence analysis of the Bregman method for the variational model of image denoising

The total variation model of Rudin, Osher, and Fatemi for image denoising is considered to be one of the best denoising models. Recently, by using the Bregman method, Goldstein and Osher obtained a very efficient algorithm for the solution of the ROF model. In this paper, we give a rigorous proof for the convergence of the Bregman method. We also indicate that a combination of the Bregman method with wavelet packet decomposition often enhances performance for certain texture rich images.