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针对低分辨率图像盲复原中信息不足的问题,可以用正则方法来求解。假设点扩散函数结构已知而参数未知,模糊矩阵可表示为带参数的形式,在Nguyen等人的正则有参盲复原框架的基础上,进一步根据Roberts交叉梯度算子构造正则项,从自适应的角度构造正则化参数,并用迭代法求解该框架的目标泛函极小值。算法分析和实验结果表明,这种方法能取得令人满意的超分辨图像复原效果。 相似文献
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Robust full Bayesian learning for radial basis networks 总被引:1,自引:0,他引:1
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提出在正则化图像恢复方法中将图像恢复结果与先验图像的最小鉴别信息作为新的正则化约束.同传统的正则化约束不同,新的约束使得恢复的图像与给定的先验图像具有最相似的灰度分布.同时给出一种自适应确定正则化参数的方法.实验结果表明,新方法在恢复效果上要优于传统的正则化方法,但对噪声则比较敏感.因此,提出在降质图像含有较多的噪声时保留传统的正则化约束,以达到更好的恢复效果. 相似文献
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现有模糊图像盲复原算法通常仅利用彩色图像的灰度信息估计模糊核,彩色图像转换成灰度图像的操作会造成信息丢失,在处理尺寸过小或显著边缘过少的图像时,模糊核的估计通常会失效,导致最后复原图像的质量不理想。针对上述问题,在新的张量框架下,把彩色模糊图像作为一个三阶张量,提出了一种基于张量总变分的模糊图像盲复原算法。首先通过调整张量总变分模型中的正则化参数获取彩色图像不同尺度的边缘信息,从而估计出模糊核;再利用张量总变分算法对模糊图像解模糊,复原出清晰图像。实验结果表明,所提算法得到的复原图像在峰值信噪比(PSNR)和主观视觉上均得到明显改善。 相似文献
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传统Lucy-Richardson(LR)算法是一种基于贝叶斯分析的图像复原迭代算法,对高信噪比的退化图像能获得很好的复原结果,但对噪声过于敏感,对低信噪比的退化图像在迭代过程中易造成噪声的放大,虽然有一些正则化方法应用到LR算法中来抑制噪声,但往往容易产生过度平滑的问题。针对这些问题将图像稀疏先验模型作为正则项引入到LR算法中,抑制噪声在迭代过程中的放大。与常规的图像梯度约束算法不同,本文算法中根据模糊图像梯度分布特点的不同提出了可变参数的图像稀疏梯度正则化约束方法,使复原图像的梯度分布参数在迭代过程中更趋近于真实梯度分布,同时通过调整正则项系数可以避免复原图像的过度平滑。实验结果表明,同标准LR算法和常规梯度约束算法相比,本文算法能够实现在抑制噪声放大的同时较好地保留图像的细节。 相似文献
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目的 为了提高运动模糊图像盲复原清晰度,提出一种混合特性正则化约束的运动模糊盲复原算法。方法 首先利用基于局部加权全变差的结构提取算法提取显著边缘,降低了噪声对边缘提取的影响。然后改进模糊核模型的平滑与保真正则项,在保证精确估计的同时,增强了模糊核的抗噪性能。最后改进梯度拟合策略,并加入保边正则项,使图像梯度更加符合重尾分布特性,且保证了边缘细节。结果 本文通过两组实验验证改进模型与所提算法的优越性。实验1以模拟运动模糊图像作为实验对象,通过对比分析5种组合步骤算法的复原效果,验证了本文改进模糊核模型与改进复原图像模型的鲁棒性较强。实验结果表明,本文改进模型复原图像的边缘细节更加清晰自然,评价指标明显提升。实验2以小型无人机真实运动模糊图像为实验对象,通过与传统算法进行对比,对比分析了所提算法的鲁棒性与实用性。实验结果表明,本文算法复原图像的标准差提升约11.4%,平均梯度提升约30.1%,信息熵提升约2.2%,且具有较好的主观视觉效果。结论 针对运动模糊图像盲复原,通过理论分析和实验验证,说明了本文改进模型的优越性,所提算法的复原效果较好。 相似文献
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Hau-San Wong Ling Guan 《Evolutionary Computation, IEEE Transactions on》2000,4(4):309-326
Image restoration is a difficult problem due to the ill-conditioned nature of the associated inverse filtering operation, which requires regularization techniques. The choice of the corresponding regularization parameter is thus an important issue since an incorrect choice would either lead to noisy appearances in the smooth regions or excessive blurring of the textured regions. In addition, this choice has to be made adaptively across, different image regions to ensure the best subjective quality for the restored image. We employ evolutionary programming (EP) to solve this adaptive regularization problem by generating a population of potential regularization strategies, and allowing them to compete under a new error measure which characterizes a large class of images in terms of their local correlational properties. The nonavailability of explicit gradient information for this measure motivates the adoption of EP techniques for its optimization, which allows efficient search at multiple error surface points. The adoption of EP also allows the broadening of the range of possible cost functions for image processing so that we can choose the most relevant function rather than the most tractable one for a particular image processing application. 相似文献
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We address the problem of constructing a nonlinear discriminant procedure based on both labeled and unlabeled data sets. A semi-supervised logistic model with Gaussian basis functions is presented along with the technique of graph-based regularization. A crucial issue in modeling process is the choice of tuning parameters included in the nonlinear semi-supervised logistic models. In order to select these adjusted parameters, we derive model selection criteria from the viewpoints of information theory and also the Bayesian approach. Some numerical examples are given to investigate the effectiveness of our proposed semi-supervised modeling strategies. 相似文献
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为能够复原出高质量的清晰图像,提出一种混合正则化约束的模糊图像盲复原方法。首先,根据模糊核的稀疏性,采用L0范数的正则项对模糊核进行稀疏约束,以提高模糊核估计的准确性;然后,根据图像梯度的稀疏性,采用混合一阶和二阶图像梯度的L0范数对图像梯度进行正则化约束,以保留图像边缘信息;最后,由于所提出的混合正则化约束模型本质上是非凸非光滑优化问题,通过交替方向乘子法对模型进行求解,并在非盲反卷积阶段采用L1范数数据拟合项和全变分的方法复原清晰图像。实验结果表明,所提方法能够复原出更加清晰的细节和边缘信息,复原结果的质量更高。 相似文献
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This paper presents kernel regularization information criterion (KRIC), which is a new criterion for tuning regularization parameters in kernel logistic regression (KLR) and support vector machines (SVMs). The main idea of the KRIC is based on the regularization information criterion (RIC). We derive an eigenvalue equation to calculate the KRIC and solve the problem. The computational cost for parameter tuning by the KRIC is reduced drastically by using the Nystro/spl uml/m approximation. The test error rate of SVMs or KLR with the regularization parameter tuned by the KRIC is comparable with the one by the cross validation or evaluation of the evidence. The computational cost of the KRIC is significantly lower than the one of the other criteria. 相似文献
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电阻抗成像中混合罚函数正则化算法的仿真研究 总被引:1,自引:0,他引:1
该文将变差函数作为罚函数引入到电阻抗成像的正则化重构算法中,从而提出了一种新的电阻抗成像算法,文中称为混合罚函数正则化算法。与常规Tikhonov正则化算法相比,该算法的突出优点是:在确保重构解适定的同时,提高重构图像的对比度和锐度,且计算量增加不大。仿真对比实验结果显示,新算法所得重构图像目标区域与背景区域之间的边界清晰,定位更加准确,与真实医学图像更加符合,这对EIT重构成像技术早日走上实用化有积极的意义。 相似文献
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A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results. 相似文献
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A neural learning approach for adaptive image restoration using afuzzy model-based network architecture 总被引:3,自引:0,他引:3
Hau-San Wong Ling Guan 《Neural Networks, IEEE Transactions on》2001,12(3):516-531
We address the problem of adaptive regularization in image restoration by adopting a neural-network learning approach. Instead of explicitly specifying the local regularization parameter values, they are regarded as network weights which are then modified through the supply of appropriate training examples. The desired response of the network is in the form of a gray level value estimate of the current pixel using weighted order statistic (WOS) filter. However, instead of replacing the previous value with this estimate, this is used to modify the network weights, or equivalently, the regularization parameters such that the restored gray level value produced by the network is closer to this desired response. In this way, the single WOS estimation scheme can allow appropriate parameter values to emerge under different noise conditions, rather than requiring their explicit selection in each occasion. In addition, we also consider the separate regularization of edges and textures due to their different noise masking capabilities. This in turn requires discriminating between these two feature types. Due to the inability of conventional local variance measures to distinguish these two high variance features, we propose the new edge-texture characterization (ETC) measure which performs this discrimination based on a scalar value only. This is then incorporated into a fuzzified form of the previous neural network which determines the degree of membership of each high variance pixel in two fuzzy sets, the EDGE and TEXTURE fuzzy sets, from the local ETC value, and then evaluates the appropriate regularization parameter by appropriately combining these two membership function values. 相似文献
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提出一种新的技术,它自适应地选取正则化参数以取得较理想的恢复效果.利用小波变换,分析正则化算子和正则化参数对图象残差的各子频段能量的影响.在本文条件下,我们论证正则化算子取拉普拉斯算子比取恒等算子恢复性能好,并且预测噪声能量.实验结果表明本文提出的方法不需要知道噪声能量,也能够自适应地确定正则化参数并且恢复性能比传统的方法好,恢复效果非常接近最优恢复. 相似文献
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一种基于L1范数正则化的回声状态网络 总被引:2,自引:0,他引:2
针对回声状态网络存在的病态解以及模型规模控制问题,本文提出一种基于L1范数正则化的改进回声状态网络.该方法通过在目标函数中添加L1范数惩罚项,提高模型求解的数值稳定性,同时借助于L1范数正则化的特征选择能力,控制网络的复杂程度,防止出现过拟合.对于L1范数正则化的求解,采用最小角回归算法计算正则化路径,通过贝叶斯信息准则进行模型选择,避免估计正则化参数.将模型应用于人造数据和实际数据的时间序列预测中,仿真结果证明了本文方法的有效性和实用性. 相似文献
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Network information criterion-determining the number of hiddenunits for an artificial neural network model 总被引:6,自引:0,他引:6
The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set. 相似文献