图像域正则化特征增强SAR成像方法

通过分析SAR成像过程和正则化特征增强方法的特点，提出了一种在图像域进行正则化特征增强的成像方法。实验结果表明，本文方法能较好地抑制旁瓣和噪声，提高SAR图像的对比度，保护目标并提高图像的分辨率，且计算量比传统方法大大减小，有效提高了计算效率。     

SAR图像幅度域增强的正则化方法

正则化方法通过增加先验信息约束,达到增强SAR图像特征的目的。在分析现有先验信息约束条件的基础上,提出SAR图像幅度域上的正则化增强方法,从理论上证明了SAR图像复数域上正则化方法和幅度域上正则化方法的等价性,进一步解释了正则化方法的相位保持特性,提高了计算效率。通过参数变换,结合先验信息,给出正则化参数的选取方法,该方法操作简单,能有效地抑制噪声,保护目标。最后利用仿真图像和MSTAR图像验证了该方法的有效性。     

基于lk范数正则化的SAR图像目标特征增强

增强SAR图像的目标特征对自动目标识别等具有重要意义。该文改进了一种基于lk范数正则化方法，并用于SAR图像目标特征增强。该方法通过开发利用符合SAR图像统计特性的先验知识，构造附加约束，把图像目标特征增强问题规划为形式简单的最优化问题，并利用一种迭代算法进行快速求解。仿真和实测数据计算结果证实了该方法的有效性。     

SAR图像相干斑抑制和特征增强的自适应正则化变分方法

研究合成孔径雷达(Synthetic Aperture Radar,SAR)图像的相干斑抑制和特征增强问题.传统的SAR图像相干斑抑制方法通常会导致边缘和目标的模糊,针对该问题,本文基于SAR图像的先验信息和处理理念,通过合理构造扩散系数和正则化参数,提出了一种新的更适合SAR图像相干斑抑制和特征增强的自适应正则化变分方法.理论分析和实验结果表明,该方法不仅能有效地抑制相干斑,而且还能有效保护并增强图像的目标和边缘特征.     

一种参数自适应正则化超分辨率图像重建算法

在正则化超分辨率重建算法中,正则化参数自适应对于抑制噪声和保持边缘非常重要.参数自适应通常是通过建立空间信息与参数的关系来实现的.在近期文献中,提出了一些空间信息自适应超分辨率重建方法,取得了较好的实验结果.然而在这些方法中,提取空间信息方法的计算量大,导致重建速度慢,限制了算法的应用.提出一种快速空间信息提取方法,并构建自适应参数模型,实验结果显示,该方法在大幅提高重建速度的同时,获得了更好的重建效果.     

一种基于多特征的距离正则化水平集快速分割方法

现有的图像分割模型存在对初始化信息敏感,分割速率慢,图像弱边界区的泄露等现象.提出了一种混合快速分割方法.该方法利用偏压场近似估计图像的局部统计信息,并结合全局信息相容性及改进的距离正则化方法建立模型,最后将模型嵌入水平集框架中,与此同时,引入双重终止准则以提高分割的速度.最后利用合成图像和真实图像进行分割实验,并与CV（Chan-Vese）模型、非线性自适应水平集方法以及局部尺度拟合模型对比,表明本方法不仅对初始化信息敏感度降低,而且分割速度提高3~5倍.     

基于一种高阶正则化模型的光流估计方法

在光流分析技术中,为了能够在特殊情况下提高 光流估计的准确性,提出一种扩展总广义变差(Ext-TGV)的 光流估计方法。方法通过引入新的高阶正则项,将经典的二阶TGV进行非局部扩展,从而有 利于实施分段 仿射,且可将柔性分割信息包含在正则项内,在光流估计中能准确提供局部运动边界以及解 决匹配项中的模糊问题; 提出一个新匹配项,使其能较好地克服光照变化和尺度变化。通过在KITTI 和SINTEL数据上的实 验结果表明,与现有的光流评估方法相比,本文提出的光流估计方法能显著的提高 光流估计的准确性。     

一种水下退化图像的增强方法

针对水下退化图像的特点,提出了一种以邻域像素的增量特征为指导的插值增强方法,根据不同的增量特征判断待插值点的插值类型,再对不同插值类型分别设计插值算法,最后对插值图像进行修正增强.实验结果表明了方法的有效性.     

一种抗噪的红外图像对比度增强方法

提出一种基于离散平稳小波变换和非线性增益的红外图像对比度增强方法。对红外图像进行离散平稳小波变换后,利用所提出的去噪方法对分辨率较好的各高频子带直接去噪;并利用所提出的非线性增益法结合文中的去噪法对分辨率较差的各高频子带进行增强。实验结果表明,提出的方法在有效地增强红外图像对比度的同时,又能很好地抑制相关噪声。算法在视觉质量上优于传统的反锐化掩膜法、直方图均衡法。     

基于正则化增强的SAR图像分割方法

SAR图像存在强烈的相干斑噪声,因此对SRN图像的分割非常困难。本文利用图像幅度的二维微分作为正则化约束对SAR图像进行增强,在抑制噪声的同时,保持了区域的边缘。对正则化增强的SAR图像进行简单的灰度门限分割,就可以取得很好的效果。试验结果表明了该方法的有效性。     

基于近邻图正则化的高光谱图像特征选择方法

高光谱图像分类是近年来的研究热点。其数据的 高维性引发了“维度灾难”问 题。数据降维成为解决问题的关键。针对高光谱数据有标记训练样本点匮乏的特点, 提出用无监督的特征选择方法对高光谱数据进行降维。该方法能够同时保持原始高光 谱数据的判别能力和局部几何结构。为了保持判别能力,用所选特征对原始高光谱数 据进行重构,利用重构误差最小化将特征选择问题转化为优化问题。为了保持局部几 何结构,建立近邻图,并将其转化为正则项加入目标函数中。通过迭代梯度下降方法 解此优化问题,得出优选特征子集参与高光谱图像分类识别任务。在真实数据集上的 实验表明,新方法能够提高分类识别的精度。     

基于TwIST-TV 约束的图像去模糊方法

传统的基于频域和小波域的去模糊算法所得的复原图像总是存在比较明显的边缘振铃及模糊效应,而较为有效的空域迭代优化去模糊算法速度通常比较慢。为了解决上述问题,提出了基于二步迭代阈值收缩（TwIST）与总变分（TV）约束相结合的图像去模糊算法（TwIST-TV）。首先在去模糊目标函数中加入对图像的TV 正则化约束,其次在对图像小波系数的每次二步迭代之前,加入对图像的TV 优化去噪约束,最后迭代获取去模糊图像。实验结果表明:相对于基于频域和小波域的模糊图像恢复算法,TwIST-TV 能有效抑制边缘模糊和振铃效应,复原图像的信噪比（SNR）、峰值信噪比（PSNR）高出1~7 dB,平均结构相似度指标（MSSIM）可高出0.05,相对于空域解卷积算法在保证求解精度相当的情况下具备6 倍以上的速度优势。     

Visual object tracking with online sample selection via lasso regularization

In the past years, discriminative methods are popular in visual tracking. The main idea of the discriminative method is to learn a classifier to distinguish the target from the background. The key step is the update of the classifier. Usually, the tracked results are chosen as the positive samples to update the classifier, which results in the failure of the updating of the classifier when the tracked results are not accurate. After that the tracker will drift away from the target. Additionally, a large number of training samples would hinder the online updating of the classifier without an appropriate sample selection strategy. To address the drift problem, we propose a score function to predict the optimal candidate directly instead of learning a classifier. Furthermore, to solve the problem of a large number of training samples, we design a sparsity-constrained sample selection strategy to choose some representative support samples from the large number of training samples on the updating stage. To evaluate the effectiveness and robustness of the proposed method, we implement experiments on the object tracking benchmark and 12 challenging sequences. The experiment results demonstrate that our approach achieves promising performance.     

一种自适应正则化技术的图像复原方法

针对降质图像的复原问题,在正则化技术解决病态性基础上提出了一种有效的自适应图像复原算法。该方法充分考虑了图像的局部特性,引入了自适应加权矩阵,采用迭代的方法改善算法的收敛性,计算中给予复原图像一定的限制。计算机仿真结果表明,该方法可有效克服模糊退化并再现了原始图像的重要信息,复原图像在峰值信噪比和主观视觉效果方面都有明显的提高。     

An improved regularization method for artifact rejection in image super-resolution

The main aim of this paper is to employ an improved regularization method to super-resolution problems. Super-resolution refers to a process that increases spatial resolution by fusing information from a sequence of images acquired in one or more of several possible ways. This process is an inverse problem, one that is known to be highly ill-conditioned. Total Variation regularization is one of the well-known techniques used to deal with such problems, which has some disadvantages like staircase effect artifacts and nonphysical dissipation. To enhance the robustness of processing against these artifacts, this paper proposes a new regularization method based on the coupling of fourth order PDE and a type of newly designed shock filtering based on complex diffusion in addition to previous Total Variation. In order to have sharp corner structures like edges, this work also considers a corner shock filter. The proposed scheme is not only able to remove the jittering effect artifacts along the edge directions but also able to restrain the rounding artifacts around the corner structures and most importantly, the stabilization of the overall process is assured.     

A Gaussian Mixture Model-based regularization method in adaptive image restoration

A GMM （Gaussian Mixture Model） based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth, edge or detail texture region according to variance-sum criteria function of the feature vectors. Then parameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network （Hopfield-NN） is used to optimize the objective function of image restoration, and network weight value matrix is updated by the output of GMM. Since GMM is used, the regularization parameters share properties of different kind of regions. In addition, the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system, and it has strong generalization capability. Comparing with non-adaptive and some adaptive image restoration algorithms, experimental results show that the proposed algorithm obtains more preferable restored images.     

A note on the method of analytical regularization

Many problems of electromagnetics are governed by singular integral equations of the first kind. As discussed by Nosich (1999), it is often possible to obtain a different equation describing the problem by applying the method of analytical regularization, and analytically inverting part of the original equation. The transformed equation is of the second kind. Therefore, as a rule, it is usually preferable to apply a numerical method to the transformed equation than to the original one. What appears to be an exception to that rule is discussed in the present paper: under proper conditions, and for a particular numerical method, results obtained by application to the transformed equation are shown to be identical to those obtained by application to the original equation. Some consequences of this equivalence are discussed     

基于广义变分正则化的红外图像噪声抑制方法

文中提出了一种广义变分正则化的红外图像噪声抑制方法,该方法采用p-范数代替目前广泛被采用的全变分范数作为正则项,构造了用于抑制图像噪声的展平泛函,从而将图像噪声抑制问题转化为能量泛函优化问题。通过推导,得到了相应的用于图像噪声抑制的非线性偏微分方程,并采用固定点迭代算法进行线性化求解,使得迭代解稳定收敛。数值试验结果表明,该方法能够有效地去除图像噪声,较之全变分图像噪声抑制方法,新方法进一步提高了对小宽度图像边缘的保持能力,是一种有效且性能优良的红外图像噪声抑制方法。     

Subspace regularization method for the single-trial estimation ofevoked potentials

A method for the single-trial estimation of the evoked potentials is proposed. The method is based on the so-called subspace regularization approach in which the second-order statistics of the set of the measurements is used to form a prior information model for the evoked potentials. The method is closely related to the Bayesian estimation. The performance of the proposed method is evaluated using realistic simulations. As a specific application the method is applied to the estimation of the target responses in the P300 test.     

A nonconvex nonsmooth regularization method with structure tensor total variation

In this paper, a novel regularization method for image restoration and reconstruction is introduced which is accomplished by adopting a nonconvex nonsmooth penalty that depends on the eigenvalues of structure tensor of the underlying image. At first, an alternating minimization scheme is developed in which the problem can be decomposed into three subproblems, two of them are convex and the remaining one is smooth. Then, the convergence of the sequence which generate by the alternating minimization algorithm is proved. Finally, the efficient performance of the proposed method is demonstrated through experimental results for both grayscale and vector-value images.