SVM based layout retargeting for fast and regularized inverse lithography

Inverse lithography technology （ILT）, also known as pixel-based optical proximity correction （PB-OPC）, has shown promising capability in pushing the current 193 nm lithography to its limit. By treating the mask optimization process as an inverse problem in lithography, ILT provides a more complete exploration of the solution space and better pattern fidelity than the tradi-tional edge-based OPC. However, the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process. To address this issue, in this paper we propose a support vector machine （SVM） based layout retargeting method for ILT, which is designed to generate a good initial input mask for the optimization process and promote the convergence speed. Supervised by optimized masks of training layouts generated by conventional ILT, SVM models are learned and used to predict the initial pixel values in the‘undefined areas’ of the new layout. By this process, an initial input mask close to the final optimized mask of the new layout is generated, which reduces iterations needed in the following optimization process. Manu-facturability is another critical issue in ILT;however, the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models. To compensate for this drawback, a spatial filter is employed to regularize the retargeted mask for complexity reduction. We implemented our layout retargeting method with a regularized level-set based ILT （LSB-ILT） algorithm under partially coherent illumination conditions. Experimental results show that with an initial input mask generated by our layout retargeting method, the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%, respectively.     

Real-time manifold regularized context-aware correlation tracking

Despite the demonstrated success of numerous correlation filter(CF)based tracking approaches,their assumption of circulant structure of samples introduces significant redundancy to learn an effective classifier.In this paper,we develop a fast manifold regularized context-aware correlation tracking algorithm that mines the local manifold structure information of different types of samples.First,different from the traditional CF based tracking that only uses one base sample,we employ a set of contextual samples near to the base sample,and impose a manifold structure assumption on them.Afterwards,to take into account the manifold structure among these samples,we introduce a linear graph Laplacian regularized term into the objective of CF learning.Fortunately,the optimization can be efficiently solved in a closed form with fast Fourier transforms(FFTs),which contributes to a highly efficient implementation.Extensive evaluations on the OTB100 and VOT2016 datasets demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms in terms of accuracy and robustness.Especially,our tracker is able to run in real-time with 28 fps on a single CPU.     

基于组合字典的图像复原约束优化算法

提出一种基于组合字典和约束优化的图像复原算法。建立表示图像复原问题的约束优化模型,其目标函数由l2保真项和双l1正则项的线性组合构成。利用交替优化技术将模型分解为多个子问题求解,并通过邻近算子解决降噪子问题。实验结果表明,与Oliverira算法和Beck算法相比,该算法的复原速度较快,所得图像质量较好,且复原图像与原始图像的均方误差较小。     

Semi-supervised learning with regularized Laplacian

We study a semi-supervised learning method based on the similarity graph and regularized Laplacian. We give convenient optimization formulation of the regularized Laplacian method and establish its various properties. In particular, we show that the kernel of the method can be interpreted in terms of discrete and continuous-time random walks and possesses several important properties of proximity measures. Both optimization and linear algebra methods can be used for efficient computation of the classification functions. We demonstrate on numerical examples that the regularized Laplacian method is robust with respect to the choice of the regularization parameter and outperforms the Laplacian-based heat kernel methods.     

Laplacian regularized robust principal component analysis for process monitoring

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA.     

一种大尺度Gauss模糊的快速采样算法

针对大模板Gauss模糊的传统算法计算复杂度较高的缺点,提出了一种基于快速采样的新算法,将计算复杂度从O(N2)降到O(N)级别.利用极大似然估计理论,对传统Gauss模板和采样模板进行分析,给出了采样算法和传统算法的联系与区别.实验表明,新算法在极大的提高算法的速度的同时,可以将两者之间的能量误差控制在1%左右,当采样点等于10N时,人眼基本无法察觉两种算法的差异.     

服从传输方程光学层析图像的正则化重建

基于近红外光子的辐射传输方程给出一种光学层析图像的正则化重建方法。通过引入图像熵和局部平滑函数为正则化项克服了重建问题的病态特性。首先阐述了基于辐射传输方程光学层析成像的前向模型，进而提出基于平滑准则的正则化重建方法。重建过程是对目标函数的优化过程。目标函数包括预测值和测量值之间的误差函数和正则化函数两部分。对上述目标函数，采用基于梯度的迭代优化方法。本文提出一种具体的基于梯度树的梯度求解算法。实验表明：该方法与非正则化重建方法相比，可有效降低重建的病态性。提高图像重建质量。     

不完全鲁棒主成分分析的正则化方法及其在背景建模中的应用

针对现有的鲁棒主成分分析(RPCA)方法忽略序列数据的连续性及不完整性的情况,提出了一种低秩矩阵恢复模型——正则化不完全鲁棒主成分分析(RIRPCA)。首先基于序列数据连续性的度量函数建立了RIRPCA模型,即最小化矩阵核范数、L₁范数和正则项的加权组合;然后使用增广拉格朗日乘子法来求解所提出的凸优化模型, 此算法具有良好的可扩展性和较低的计算复杂度;最后,将RIRPCA应用到视频背景建模中。实验结果表明,RIRPCA比矩阵补全和不完全RPCA等方法在恢复丢失元素和分离前景上具有优越性。     

基于稀疏正则化和变量分裂的图像去模糊

传统的图像去模糊方法易产生振铃和边缘模糊等“伪像”效应,针对这一问题,采用非光滑的正则项约束图像在稀疏字典下表示系数的稀疏性,并引入非负约束项,提出了图像的稀疏正则化去模糊模型。进一步,基于交替方向拉格朗日乘子算法,提出了求解该模型的多变量分裂迭代快速算法,将复杂问题求解转化为三个简单子问题的迭代求解,降低了模型求解的复杂性。实验结果表明,所提出的去模糊模型及其快速算法相对较好地保持了图像的结构特征和平滑性,并降低了计算复杂性。     

Graph regularized discriminative non-negative matrix factorization for face recognition

Non-negative matrix factorization (NMF) has been widely employed in computer vision and pattern recognition fields since the learned bases can be interpreted as a natural parts-based representation of the input space, which is consistent with the psychological intuition of combining parts to form a whole. In this paper, we propose a novel constrained nonnegative matrix factorization algorithm, called the graph regularized discriminative non-negative matrix factorization (GDNMF), to incorporate into the NMF model both intrinsic geometrical structure and discriminative information which have been essentially ignored in prior works. Specifically, both the graph Laplacian and supervised label information are jointly utilized to learn the projection matrix in the new model. Further we provide the corresponding multiplicative update solutions for the optimization framework, together with the convergence proof. A series of experiments are conducted over several benchmark face datasets to demonstrate the efficacy of our proposed GDNMF.     

基于Laplacian正则化最小二乘的半监督SAR目标识别

提出了一种基于核主成分分析(kernel principal component analysis,简称KPCA)和拉普拉斯正则化最小二乘(Laplacian regularized least squares,简称LapRLS)的合成孔径雷达(synthetic aperture radar,简称SAR)目标识别方法.KPCA特征提取方法不仅能够提取目标主要特征,而且有效地降低了特征维数.Laplacian正则化最小二乘分类是一种半监督学习方法,将训练集样本作为有标识样本,测试集样本作为无标识样本,在学习过程中将测试集样本包含进来以获得更高的识别率.在MSTAR实测SAR地面目标数据上进行实验,结果表明,该方法具有较高的识别率,并对目标角度间隔具有鲁棒性.与模板匹配法、支撑矢量机以及正则化最小二乘监督学习方法相比,具有更高的SAR目标识别正确率.此外,还通过实验分析了不同情况下有标识样本数目对目标识别性能的影响.     

联合Laplacian正则项和特征自适应的数据聚类算法

在信息爆炸时代,大数据处理已成为当前国内外热点研究方向之一.谱分析型算法因其特有的性能而获得了广泛的应用,然而受维数灾难影响,主流的谱分析法对高维数据的处理仍是一个极具挑战的问题.提出一种兼顾维数特征优选和图Laplacian约束的聚类模型,即联合拉普拉斯正则项和自适应特征学习（joint Laplacian regularization and adaptive feature learning,简称LRAFL）的数据聚类算法.基于自适应近邻进行图拉普拉斯学习,并将低维嵌入、特征选择和子空间聚类纳入同一框架,替换传统谱聚类算法先图Laplacian构建、后谱分析求解的两级操作.通过添加非负加和约束以及低秩约束,LRAFL能获得稀疏的特征权值向量并具有块对角结构的Laplacian矩阵.此外,提出一种有效的求解方法用于模型参数优化,并对算法的收敛性、复杂度以及平衡参数设定进行了理论分析.在合成数据和多个公开数据集上的实验结果表明,LRAFL在效果效率及实现便捷性等指标上均优于现有的其他数据聚类算法.     

基于超图正则化非负Tucker分解的图像聚类算法

针对非负张量分解应用于图像聚类时忽略了高维数据内部几何结构的问题,在经典的张量非负Tucker分解的基础上,添加超图正则项以尽可能多地保留原始数据的内在几何结构信息,提出一种基于超图正则化非负Tucker分解模型HGNTD。通过构造超图刻画数据内部样本间的高阶关系,提高几何结构描述的准确性,针对超图正则化非负张量分解模型,基于交替非负最小二乘法,设计快速有效的超图正则化非负Tucker分解算法求解所给模型,证明算法在非负的条件下是收敛的,最终将算法应用于图像聚类。在Yale和COIL两个常用公开数据集上的实验结果表明,相对于k-means、非负矩阵分解、图正则化非负矩阵分解、非负Tucker分解和图正则化非负Tucker分解等算法,超图正则化非负Tucker分解算法聚类准确度提升了8.6%~11.4%,归一化互信息提升了2.0%~7.5%,具有更好的聚类效果。     

一种快速检测图像角点特征的线搜索式方法

传统的图像角点特征检测方法在速度和准确性两方面难以兼顾. 针对该问题, 提出了一种角点特征检测的线搜索式方法. 该方法作用于一个以当前像素为中心核的圆掩模, 在该掩模内搜索通过核的所有直线, 如果存在一条直线不穿过核附近给定邻域以外的其他同值收缩核(Univalue segment assimilating nucleus, USAN)区域, 则当前像素点为角点. 论文论证了使用有限数目搜索线的可行性与必要性. 采用由粗及细的搜索策略, 动态设计搜索线的数目与搜索线上的检测点数目, 以提高检测速度. 提出了一种基于最大同值距离的新型非极大值抑制进行角点的精确定位, 并结合多种新型伪响应抑制措施, 有效地提高了算法的准确度. 实验结果表明该方法在准确性方面优于MIC、SUSAN和Harris等算法, 而且速度快, 仅稍慢于MIC算法, 具有优良的综合性能.     

Topology optimization of shell-infill structures using a distance regularized parametric level-set method

This paper presents a novel topology optimization formulation for shell-infill structures based on a distance regularized parametric level-set method (PLSM). In this method, the outer shell and the infill are represented by two distinct level sets of a single-level set function (LSF). In order to obtain a controllable and uniform shell thickness, a distance regularization (DR) term is introduced to formulate a weighted bi-objective function. The DR term is minimized along with the original objective, regularizing the parametric LSF close to a signed distance function. With the signed distance property, the area between the two-level sets can be contoured as the shell with a uniform thickness. Additionally, the presented formulation retains one important merit of the PLSM that new holes are able to nucleate during the optimization process. With respect to the material of the shell, the infill is filled with a weaker and lighter material with tunable parameters. Particularly, the infill can be pre-designed with isotropic microstructures. Three compliance minimization examples are provided to demonstrate the effectiveness of this formulation.

     

多帧图像的Tikhonov正则化重建算法研究*

介绍了Tikhonov正则化超分辨率重建算法的基本原理和特点,在原有正则化空域图像复原方法的基础上,根据多帧序列图像之间的互补信息,提出一种改进的正则化空域图像复原的新方法,该算法直接将正则化函数作用于图像超分辨率重建算法的条件概率项内,提高了正则化项的校正效率,并用共轭梯度运算来改善算法的收敛性,节省了图像重建所需的时间。实验和仿真结果表明,与传统方法相比,该算法不仅减轻了图像边缘纹理的模糊性,提高了图像的清晰度,而且收敛速度快。     

基于掩蔽估计与优化的单通道语音增强算法

单通道语音增强算法通过从带噪语音中估计并抑制噪声成分来得到增强语音。然而，噪声估计算法在计算时存在过估现象，导致部分估计噪声能量值比实际值大。尽管可以通过补偿消去这些过估值，但引入的误差同样会降低增强语音的整体质量。针对此问题，提出一种基于计算听觉场景分析（CASA）的时频掩蔽估计与优化算法。首先，通过直接判决（DD）算法估计先验信噪比（SNR）并计算初始掩蔽；其次，利用噪声与带噪语音在Gammatone频带内的互相关（ICC）系数来计算噪声的存在概率，结合带噪语音能量谱得到新的噪声估计，减少原估计噪声中的过估成分；然后，利用优化算法对初始掩蔽进行迭代处理以减少其中因噪声过估而存在的误差并增加其中的目标语音成分，在满足条件后停止迭代并得到新的掩蔽；最后，利用新的掩蔽合成增强语音。实验结果表明在不同的背景噪声下，相比优化前，新的掩蔽使增强语音获得了较高的主观语音质量（PESQ）和语音可懂度（STOI）值，提升了语音听感与可懂度。     

Hessian optimal design for image retrieval

Recently there has been a considerable interest in active learning from the perspective of optimal experimental design (OED). OED selects the most informative samples to minimize the covariance matrix of the parameters, so that the expected prediction error of the parameters, as well as the model output, can be minimized. Most of the existing OED methods are based on either linear regression or Laplacian regularized least squares (LapRLS) models. Although LapRLS has shown a better performance than linear regression, it suffers from the fact that the solution is biased towards a constant and the lack of extrapolating power. In this paper, we propose a novel active learning algorithm called Hessian optimal design (HOD). HOD is based on the second-order Hessian energy for semi-supervised regression which overcomes the drawbacks of Laplacian based methods. Specifically, HOD selects those samples which minimize the parameter covariance matrix of the Hessian regularized regression model. The experimental results on content-based image retrieval have demonstrated the effectiveness of our proposed approach.     

图拉普拉斯正则化稀疏变换学习图像去噪算法

从噪声图像中恢复干净的图像是对图像进行有效处理与分析的首要前提之一,而去除噪声的同时保持图像的特征则是图像去噪的一个具有挑战性的问题.为了在去除噪声的同时尽量保持图像的局部结构特征,提出了一种基于图拉普拉斯正则化稀疏变换学习的图像去噪算法.通过引入图拉普拉斯正则化对邻域像素进行约束,可以较好地保护相邻像素之间的相关性,...     

Regularized nonnegative matrix factorization using Gaussian mixture priors for supervised single channel source separation

We introduce a new regularized nonnegative matrix factorization (NMF) method for supervised single-channel source separation (SCSS). We propose a new multi-objective cost function which includes the conventional divergence term for the NMF together with a prior likelihood term. The first term measures the divergence between the observed data and the multiplication of basis and gains matrices. The novel second term encourages the log-normalized gain vectors of the NMF solution to increase their likelihood under a prior Gaussian mixture model (GMM) which is used to encourage the gains to follow certain patterns. In this model, the parameters to be estimated are the basis vectors, the gain vectors and the parameters of the GMM prior. We introduce two different ways to train the model parameters, sequential training and joint training. In sequential training, after finding the basis and gains matrices, the gains matrix is then used to train the prior GMM in a separate step. In joint training, within each NMF iteration the basis matrix, the gains matrix and the prior GMM parameters are updated jointly using the proposed regularized NMF. The normalization of the gains makes the prior models energy independent, which is an advantage as compared to earlier proposals. In addition, GMM is a much richer prior than the previously considered alternatives such as conjugate priors which may not represent the distribution of the gains in the best possible way. In the separation stage after observing the mixed signal, we use the proposed regularized cost function with a combined basis and the GMM priors for all sources that were learned from training data for each source. Only the gain vectors are estimated from the mixed data by minimizing the joint cost function. We introduce novel update rules that solve the optimization problem efficiently for the new regularized NMF problem. This optimization is challenging due to using energy normalization and GMM for prior modeling, which makes the problem highly nonlinear and non-convex. The experimental results show that the introduced methods improve the performance of single channel source separation for speech separation and speech–music separation with different NMF divergence functions. The experimental results also show that, using the GMM prior gives better separation results than using the conjugate prior.