共查询到19条相似文献,搜索用时 187 毫秒
1.
2.
3.
4.
模糊图像恢复是数字图像处理领域的研究热点之一,总变差(Total Variation, TV)规整化可以很好的保持图像的细节,然而,传统的TV图像恢复模型需要考虑最优的正则化参数,由此,提出了一族包含不同规整化因子,带总观测误差约束的模糊图像恢复模型,并分为去模糊和去噪两步求解此模型。在去模糊过程中,利用共轭梯度法求出一个满足总观测误差约束的初始恢复图像;在去噪过程中,首先,以去模糊的结果作为初始估计;其次,针对 范数最小化问题,利用优化—最小化(Majoriziation-Minimization, MM)算法的思想,将原问题转化为一系列容易求解的优化子问题;最后,极小化优化子问题,得到最终的恢复图像。实验结果表明,该算法对模糊图像的恢复效果是显著地。 相似文献
5.
利用Hessian核范数进行图像复原是目前较好的高阶正则化方法,但是由于Hessian核范数正则项的高度非线性和不可微性,图像去模糊和去噪过程耦合度高,求解算法的复杂度高.本文利用变量分裂设计了一种具有闭解形式的交替迭代最小化快速图像复原算法,将图像去模糊、去噪分步进行,并给出算法的收敛性证明.实验结果表明,本文方法不仅在峰值信噪比方面优于原有的基于Hessian核范数图像复原的主优化(Majorization-Minimization,MM)方法,而且大大降低了算法的迭代次数和运行时间. 相似文献
6.
7.
8.
传统的加权最小二乘法、惩罚项加权最小二乘法虽然能够重建得到较好质量的图像,但在欠采样的条件下不能很好的拟制噪声.全变差作为正则项已广泛用于图像重建中,利用图像稀疏的先验知识能够在欠采样的条件下很好的重建图像.本文结合加权最小二乘法和全变差的优点,构造了基于全变差正则项的加权最小二乘法目标函数,运用交替求解的方法,将目标函数分解为求解二次优化和全变差正则化的优化问题,并分别用超松弛迭代方法和梯度下降法求解这两个优化问题.采用Zubal模型对该算法与传统算法进行仿真验证比较,并用相关系数、方差、信噪比等参数描述图像重建质量.结果表明在欠采样条件下,该算法能够更好的拟制噪声,重构效果比传统的有明显地提高. 相似文献
9.
10.
11.
当前去模糊方法只利用图像单一的稀疏特性作为先验信息,忽略了伪边缘(如振铃瑕疵)对模糊核估计的影响,导致其去模糊性能不佳.本文充分利用复杂结构图像的先验信息,设计了振铃约束下的全变差正则化图像去模糊算法.首先,利用多分辨率图像金字塔策略建立多层图像模型,通过对比模糊图像和潜在清晰图像来获得振铃先验信息.其次,将振铃正则约... 相似文献
12.
针对基于传统全变分(TV)模型的图像压缩感知(CS)重建算法不能有效地恢复图像的细节和纹理,从而导致图像过平滑的问题,该文提出一种基于结构组全变分(SGTV)模型的图像压缩感知重建算法。该算法利用图像的非局部自相似性和结构稀疏特性,将图像的重建问题转化为由非局部自相似图像块构建的结构组全变分最小化问题。算法以结构组全变分模型为正则化约束项构建优化模型,利用分裂Bregman迭代将算法分离成多个子问题,并对每个子问题高效地求解。所提算法很好地利用了图像自身的信息和结构稀疏特性,保护了图像细节和纹理。实验结果表明,该文所提出的算法优于现有基于全变分模型的压缩感知重建算法,在PSNR和视觉效果方面取得了显著提升。 相似文献
13.
基于稀疏表示的图像复原算法大都只利用了图像整体稀疏性和局部稀疏性中的一种,未充分利用图像的先验知识,基于此,本文在稀疏表示框架下,同时引入Cosparse解析模型及平移不变小波变换两种稀疏模型,前者对每个图像块进行稀疏表示,后者对整幅图像进行稀疏表示,从而提出一种新的图像复原算法。该算法将图像复原问题归结为双稀疏正则化问题。为求解复杂的双稀疏优化问题,本文运用交替方向乘子法 (ADMM, Alternating Direction Method of Multipliers)算法将该约束优化问题分解为若干子问题,通过交替迭代求解获得复原图像。实验中对不同类型的模糊图像进行了复原,其结果表明该算法对于各类模糊图像的复原比现有复原算法效果更好,从而验证了算法的有效性。 相似文献
14.
Chongyang Zhang Weiyao Lin Wei Li Bing Zhou Jun Xie Jijia Li 《Signal Processing: Image Communication》2013,28(9):1171-1186
Image deblurring techniques play important roles in many image processing applications. As the blur varies spatially across the image plane, it calls for robust and effective methods to deal with the spatially-variant blur problem. In this paper, a Saliency-based Deblurring (SD) approach is proposed based on the saliency detection for salient-region segmentation and a corresponding compensate method for image deblurring. We also propose a PDE-based deblurring method which introduces an anisotropic Partial Differential Equation (PDE) model for latent image prediction and employs an adaptive optimization model in the kernel estimation and deconvolution steps. Experimental results demonstrate the effectiveness of the proposed algorithm. 相似文献
15.
An asynchronous distributed cross-layer optimization (ADCO) method was proposed to solve the problem of jointly considering real-time routing,rate allocation and power control in FANET (flying ad hoc network).And a delay-constrained cross-layer optimization framework was designed to formally represent proposed problem.Then Lagrangian relaxation and dual decomposition methods was used to divide joint optimization problem into several sub-problems.ADCO allowed each relay node to perform the optimization operation for different sub-problems with local information,and the relay nodes could update the dual variables based on asynchronous update mechanism.The simulation results show that the proposed algorithm can improve the network performance effectively in terms of energy efficiency,packet timeout ratio and network throughput. 相似文献
16.
This paper studies image deblurring problems using a total variation-based model, with a non-negativity constraint. The addition of the non-negativity constraint improves the quality of the solutions, but makes the solution process a difficult one. The contribution of our work is a fast and robust numerical algorithm to solve the non-negatively constrained problem. To overcome the nondifferentiability of the total variation norm, we formulate the constrained deblurring problem as a primal-dual program which is a variant of the formulation proposed by Chan, Golub, and Mulet for unconstrained problems. Here, dual refers to a combination of the Lagrangian and Fenchel duals. To solve the constrained primal-dual program, we use a semi-smooth Newton's method. We exploit the relationship between the semi-smooth Newton's method and the primal-dual active set method to achieve considerable simplification of the computations. The main advantages of our proposed scheme are: no parameters need significant adjustment, a standard inverse preconditioner works very well, quadratic rate of local convergence (theoretical and numerical), numerical evidence of global convergence, and high accuracy of solving the optimality system. The scheme shows robustness of performance over a wide range of parameters. A comprehensive set of numerical comparisons are provided against other methods to solve the same problem which show the speed and accuracy advantages of our scheme. 相似文献
17.
模糊图像可表示为清晰图像和模糊核函数的卷积,由模糊图像恢复出清晰图像,需要同时估计模糊核和清晰图像,因此是一个病态问题.优化含有先验项的代价函数是求解病态问题最有效方法之一.针对图像盲去模糊问题,本研究提出具有更强稀疏表达能力的凹凸范数比值正则化先验项,在用变量分裂法求解模型时,提出用L1范数保真项更新估计图像,在更新模糊核时,提出使用线性递增权重参数对模糊核按多尺度方法由粗到细逐步估计,当获得模糊核后,利用封闭阈值公式估计清晰图像.该方法能快速得到高质量的清晰图像,实验结果验证了模型的有效性和算法的快速性. 相似文献
18.
Blind deblurring, typically underdetermined or ill-posed problem, has attracted numerous research studies over the recent years. Various priors of either the image or the blur kernel are proposed to establish various regularization models to estimate the blur kernel. And sharp edges are often employed as an important clue to recover the blur kernel. However, due to the harmful effects caused by textures and various artifacts, sharp edges are not always beneficial to the kernel estimation. To address this problem, this paper presents a step-edge based blind image deblurring algorithm using steerable gradients. The proposed algorithm adopts a coarse-to-fine multiscale framework with step-edge restoration, kernel estimation and latent image estimation. In each scale, the step-edges are detected and refined through fast image decomposition and thresholding on steerable gradients, while the kernel and latent image are estimated by minimizing the quadratic energy functionals with steerable gradients. Because each of the minimizing functional has a closed-form solution, and can be implemented by using FFTs, our algorithm is also very fast. Experimental results on both synthetic and real data demonstrate that our method outperforms most existing single image blind deblurring methods. 相似文献