Automated Regularization Parameter Selection in Multi-Scale Total Variation Models for Image Restoration

Multi-scale total variation models for image restoration are introduced. The models utilize a spatially dependent regularization parameter in order to enhance image regions containing details while still sufficiently smoothing homogeneous features. The fully automated adjustment strategy of the regularization parameter is based on local variance estimators. For robustness reasons, the decision on the acceptance or rejection of a local parameter value relies on a confidence interval technique based on the expected maximal local variance estimate. In order to improve the performance of the initial algorithm a generalized hierarchical decomposition of the restored image is used. The corresponding subproblems are solved by a superlinearly convergent algorithm based on Fenchel-duality and inexact semismooth Newton techniques. The paper ends by a report on numerical tests, a qualitative study of the proposed adjustment scheme and a comparison with popular total variation based restoration methods.     

Model Selection Using Information Criteria and Genetic Algorithms

Automated model searches using information criteria are used for the estimation of linear single equation models. Genetic algorithms are described and used for this purpose. These algorithms are shown to be a practical method for model selection when the number of sub-models are very large. Several examples are presented including tests for bivariate Granger causality and seasonal unit roots. Automated selection of an autoregressive distributed lag model for the consumption function in the US is also undertaken.JEL classifications: C32, C69     

基于小波域隐马尔可夫树模型的图像复原

从图像复原的Bayesian方法出发,提出一种基于小波域隐马尔可夫树(HMT)模型的线性图像复原算法,小波域HMT模型采用混合高斯模型刻画各子带系数的概率分布,并通过小波系数隐状态在多个尺度之间的Markov依赖性来刻画自然图像小波系数随尺度减小而指数衰减的特性,由于小波域HMT模型准确刻画了自然图像小波变换的统计特性,该文算法以此作为自然图像的先验模型,将图像复原问题转化为一个约束优化问题并用最速下降法对其进行求解,同时,提出了一种规整化参数和HMT模型参数的自适应选择方法,实验结果表明,基于小波域HMT模型的图像复原算法较好地再现了各种边缘信息,复原出的图像在信噪比和视觉效果方面都有明显的提高。     

一种基于PDE的图像复原模型

给出了图像处理中去除乘性噪声的一种方法,详细阐述了乘性噪声的全变分复原模型的构造,用变分法得到了与之相应的偏微分方程,并用直接差分法对该模型进行数值仿真.实验结果表明,本文给出的方法不仅去噪效果良好,而且能很好地保持图像的边缘纹理特征.     

正则图像恢复中正则化算子选取的定性分析

对病态图像恢复中正则化算子的选取问题进行定性分析。以最小化正则解模糊误差为目标,利用泰勒级数定性地分析怎样选取正则化算子,得出结论:在信号的强度大于噪声强度的频带正则化算子应该是带阻的,而信号的强度小于噪声强度的频带正则化算子应该是带通的,通常情况下应选取低阻高通的正则化算子。实验结果表明了该结论的正确性。     

图像修复RBF模型的Kalman改进算法

利用Kalman滤波器训练图像修复RBF网络模型的权值与基函数的中心,由于利用滤波器测量噪声的协方差阵实现整个模型的图像降噪功能,新算法边缘修复能力较强．本文通过仿真,检验了新模型的降噪能力;结果证明,新算法较一般的堆纳滤波器,均值滤波降噪能力明显提高。     

Bregmanized Domain Decomposition for Image Restoration

Computational problems of large-scale data are gaining attention recently due to better hardware and hence, higher dimensionality of images and data sets acquired in applications. In the last couple of years non-smooth minimization problems such as total variation minimization became increasingly important for the solution of these tasks. While being favorable due to the improved enhancement of images compared to smooth imaging approaches, non-smooth minimization problems typically scale badly with the dimension of the data. Hence, for large imaging problems solved by total variation minimization domain decomposition algorithms have been proposed, aiming to split one large problem into N>1 smaller problems which can be solved on parallel CPUs. The N subproblems constitute constrained minimization problems, where the constraint enforces the support of the minimizer to be the respective subdomain. In this paper we discuss a fast computational algorithm to solve domain decomposition for total variation minimization. In particular, we accelerate the computation of the subproblems by nested Bregman iterations. We propose a Bregmanized Operator Splitting–Split Bregman (BOS-SB) algorithm, which enforces the restriction onto the respective subdomain by a Bregman iteration that is subsequently solved by a Split Bregman strategy. The computational performance of this new approach is discussed for its application to image inpainting and image deblurring. It turns out that the proposed new solution technique is up to three times faster than the iterative algorithm currently used in domain decomposition methods for total variation minimization.     

基于正则模型的SAR图像自适应目标特征增强方法

研究SAR图像特征增强的自适应阈值选取方法.本文采用一种新的度量函教表示SAR图像的稀疏先验,建立正则化模型,证明了不动点迭代求解的收敛性.然后将SAR相位历史数据转化为复数据,得到复数域正则解的解析表示式.该方法不需要迭代,简化了求解过程,并且将正则化参数的确定归结为阈值的选择问题.最后基于广义交叉检验准则实现了阈值的自适应选取.实验中采用目标杂波比来衡量处理效果,实验结果说明本文方法能快速有效地实现SAR图像特征增强.     

一种基于粒子群优化算法的图像盲复原方法

传统的图像盲复原算法通常采用模糊图像与复原图像的均方误差作为优化的性能指标.为进一步提高复原效果,结合反映人类视觉特性的Weber定律,提出一种改进的图像盲复原优化性能指标,并且采用双粒子群交替最小化进行求解,即在模糊辨识阶段,采用一个粒子群优化算法求解点传播函数;在复原阶段,采用另一个粒子群优化算法求解复原图像.仿真实验表明,提出的算法比以前的算法有更好的复原效果.     

湍流退化图像并行校正方法

文章对湍流退化图像的双重循环迭代图像复原算法的计算流程进行了分析,将算法的串行计算模式转换为并行模式,提出了基于并行模式的多结点并行处理方法。成功地实现算法的并行计算模式到多结点结构的有效映射,对算法的程序代码进行了有效移植,在多结点系统上运行通过。实验结果表明,不仅提出的并行校正方法有效,而且提高了图像复原处理帧频数。     

图像复原技术研究

在对图像复原技术进行简单介绍的基础上,对图像的退化、复原过程中的数学模型进行了一定的论述,然后在此基础上对图像噪声处理与去除的几种方法进行了重点研究.     

Robust Detection of Phone Boundaries Using Model Selection Criteria With Few Observations

Automatic phone segmentation techniques based on model selection criteria are studied. We investigate the phone boundary detection efficiency of entropy- and Bayesian- based model selection criteria in continuous speech based on the DISTBIC hybrid segmentation algorithm. DISTBIC is a text-independent bottom-up approach that identifies sequential model changes by combining metric distances with statistical hypothesis testing. Using robust statistics and small sample corrections in the baseline DISTBIC algorithm, phone boundary detection accuracy is significantly improved, while false alarms are reduced. We also demonstrate further improvement in phonemic segmentation by taking into account how the model parameters are related in the probability density functions of the underlying hypotheses as well as in the model selection via the information complexity criterion and by employing M-estimators of the model parameters. The proposed DISTBIC variants are tested on the NTIMIT database and the achieved F ₁ measure is 74.7% using a 20-ms tolerance in phonemic segmentation.     

基于整体变分图象恢复模型的小波域正则化方法

通过改进小波软阈值收缩的过程,提出了小波框架下的整体变分正则化模型.模型中利用改进小波阈值技术选取合适的需要保留的小波系数,然后对该系数重建图象进行空域各向异性扩散处理.文中提出了该变分问题完整的空域和小波域交替迭代的图象恢复算法.实验证明,该算法不仅具有较强的噪声抑制能力,而且能够保持和增强图象的边缘,大大提高了图象的视觉质量.     

Stopping Criteria for Anisotropic PDEs in Image Processing

A number of nonlinear diffusion-like equations have been proposed for filtering noise, removing blurring and other applications. These equations are usually developed as time independent equations. An artificial time is introduced to change these equations to parabolic type equations which are then marched to a steady state. In practice the time iteration is stopped before the steady state is reached. The time when to stop the iteration is usually determined manually for each case. In this study we develop a more automatic procedure for stopping the time integration.     

Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration

Recently, much attention has been spent on neural architecture search (NAS), aiming to outperform those manually-designed neural architectures on high-level vision recognition tasks. Inspired by the success, here we attempt to leverage NAS techniques to automatically design efficient network architectures for low-level image restoration tasks. In particular, we propose a memory-efficient hierarchical NAS (termed HiNAS) and apply it to two such tasks: image denoising and image super-resolution. HiNAS adopts gradient based search strategies and builds a flexible hierarchical search space, including the inner search space and outer search space. They are in charge of designing cell architectures and deciding cell widths, respectively. For the inner search space, we propose a layer-wise architecture sharing strategy, resulting in more flexible architectures and better performance. For the outer search space, we design a cell-sharing strategy to save memory, and considerably accelerate the search speed. The proposed HiNAS method is both memory and computation efficient. With a single GTX1080Ti GPU, it takes only about 1 h for searching for denoising network on the BSD-500 dataset and 3.5 h for searching for the super-resolution structure on the DIV2K dataset. Experiments show that the architectures found by HiNAS have fewer parameters and enjoy a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. Code is available at: https://github.com/hkzhang91/HiNAS

     

Criteria for Software Reliability Model Comparisons

A set of criteria is proposed for the comparison of software reliability models. The intention is to provide a logically organized basis for determining the superior models and for the presentation of model characteristics. It is hoped that in the future, a software manager will be able to more easily select the model most suitable for his/her requirements from among the preferred ones.     

一种运动模糊图像的复原法

被摄物与相机之间的相对运动往往会造成图像的运动模糊，给图像的识别带来不便。为了恢复这些模糊图像，文中提出了一种运动模糊图像的复原法。它利用了双线性插值及模糊方向上微分图像灰度值(绝对值)之和最小的方法来求出运动模糊的方向，其次运用传播波方程对任意方向上的匀速直线运动模糊进行恢复。将此方法应用于一个具体的实例当中，发现效果明显，由此可看出此方法是十分有效的。