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1.
Satellite Image Deblurring Using Complex Wavelet Packets   总被引:3,自引:0,他引:3  
The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem. Direct inversion leads to unacceptable noise amplification. Usually the problem is regularized during the inversion process. Recently, new approaches have been proposed, in which a rough deconvolution is followed by noise filtering in the wavelet transform domain. Herein, we have developed this second solution, by thresholding the coefficients of a new complex wavelet packet transform; all the parameters are automatically estimated. The use of complex wavelet packets enables translational invariance and improves directional selectivity, while remaining of complexity O(N). A new hybrid thresholding technique leads to high quality results, which exhibit both correctly restored textures and a high SNR in homogeneous areas. Compared to previous algorithms, the proposed method is faster, rotationally invariant and better takes into account the directions of the details and textures of the image, improving restoration. The images deconvolved in this way can be used as they are (the restoration step proposed here can be inserted directly in the acquisition chain), and they can also provide a starting point for an adaptive regularization method, enabling one to obtain sharper edges.  相似文献
2.
Are Edges Incomplete?   总被引:3,自引:0,他引:3  
We address the problem of computing a general-purpose early visual representation that satisfies two criteria. 1) Explicitness: To be more useful than the original pixel array, the representation must take a significant step toward making important image structure explicit. 2) Completeness: To support a diverse set of high-level tasks, the representation must not discard information of potential perceptual relevance. The most prevalent representation in image processing and computer vision that satisfies the completeness criterion is the wavelet code. In this paper, we propose a very different code which represents the location of each edge and the magnitude and blur scale of the underlying intensity change. By making edge structure explicit, we argue that this representation better satisfies the first criterion than do wavelet codes. To address the second criterion, we study the question of how much visual information is lost in the representation. We report a novel method for inverting the edge code to reconstruct a perceptually accurate estimate of the original image, and thus demonstrate that the proposed representation embodies virtually all of the perceptually relevant information contained in a natural image. This result bears on recent claims that edge representations do not contain all of the information needed for higher level tasks.  相似文献
3.
基于调和模型的快速神经网络图像复原算法   总被引:3,自引:0,他引:3       下载免费PDF全文
针对传统神经网络图像复原算法在复原过程中模糊图像边缘,收敛速度慢等不足,提出一种基于调和模型的快速神经网络图像复原算法.在该算法中,图像复原模型的正则化项采用调和模型,并在每次网络状态更新时引入最陡下降方法,使得网络能量迅速减小.实验表明,提出的算法能够很好复原图像的边缘特征,并具有快速收敛等优点.  相似文献
4.
A Note on Two Classical Enhancement Filters and Their Associated PDE's   总被引:2,自引:0,他引:2  
We establish in 2D, the P.D.E. associated with a classical image enhancement filter, the Kramer operator and compare it with another classical shock filter, the Osher-Rudin filter. We show that each one corresponds to a non-flat mathematical morphology operator conditioned by a the sign of an edge detector. In the case of the Kramer operator, the equation is conditioned by the Canny edge detector while in the case of the original Rudin-Osher filter, the equation is conditioned by the sign of the Laplacian.  相似文献
5.
改进的广义高斯分布与非局部均值图像去模糊   总被引:2,自引:0,他引:2       下载免费PDF全文
为了改善常规算法不能保留图像边缘细节信息的缺陷,获得更好的图像去模糊效果,在非局部均值图像复原算法的基础上提出一种新的基于广义高斯分布与非局部均值的去模糊算法。先对模糊图像进行小波变换,然后应用极大似然估计的方法以及经典的Newton-Raphson算法来估计出广义高斯分布模型的尺度参数和形状参数,利用这两个参数改进原始的单一根据指数函数的衰减速度和局限于一个参数来求图像权值的方法。在多个典型图像上的测试结果表明,改进算法后的图像去模糊化效果比原始的NL-means方法更优越,具有很好的应用前景。  相似文献
6.
Variational Image Restoration and Decomposition with Curvelet Shrinkage   总被引:2,自引:1,他引:1  
The curvelet is more suitable for image processing than the wavelet and able to represent smooth and edge parts of image with sparsity. Based on this, we present a new model for image restoration and decomposition via curvelet shrinkage. The new model can be seen as a modification of Daubechies-Teschke’s model. By replacing the B p,q β term by a G p,q β term, and writing the problem in a curvelet framework, we obtain elegant curvelet shrinkage schemes. Furthermore, the model allows us to incorporate general bounded linear blur operators into the problem. Various numerical results on denoising, deblurring and decomposition of images are presented and they show that the model is valid.
Lingling JiangEmail:
  相似文献
7.
A hybrid learning system for image deblurring   总被引:1,自引:0,他引:1  
Min  Mitra   《Pattern Recognition》2002,35(12):2881-2894
In this paper we propose a 3-stage hybrid learning system with unsupervised learning to cluster data in the first stage, supervised learning in the middle stage to determine network parameters and finally a decision-making stage using voting mechanism. We take this opportunity to study the role of various supervised learning systems that constitute the middle stage. Specifically, we focus on one-hidden layer neural network with sigmoidal activation function, radial basis function network with Gaussian activation function and projection pursuit learning network with Hermite polynomial as the activation function. These learning systems rank in increasing order of complexity. We train and test each system with identical data sets. Experimental results show that learning ability of a system is controlled by the shape of the activation function when other parameters remain fixed. We observe that clustering in the input space leads to better system performance. Experimental results provide compelling evidences in favor of use of the hybrid learning system and the committee machines with gating network.  相似文献
8.
Image Deblurring in the Presence of Impulsive Noise   总被引:1,自引:0,他引:1  
Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The main challenge is to deblur the image, recover its discontinuities and at the same time remove the impulse noise. Median-based approaches are inadequate, because at high noise levels they induce nonlinear distortion that hampers the deblurring process. Distinguishing outliers from edge elements is difficult in current gradient-based edge-preserving restoration methods. The suggested approach integrates and extends the robust statistics, line process (half quadratic) and anisotropic diffusion points of view. We present a unified variational approach to image deblurring and impulse noise removal. The objective functional consists of a fidelity term and a regularizer. Data fidelity is quantified using the robust modified L 1 norm, and elements from the Mumford-Shah functional are used for regularization. We show that the Mumford-Shah regularizer can be viewed as an extended line process. It reflects spatial organization properties of the image edges, that do not appear in the common line process or anisotropic diffusion. This allows to distinguish outliers from edges and leads to superior experimental results.  相似文献
9.
一种快速的QR码图像去模糊方法   总被引:1,自引:0,他引:1       下载免费PDF全文
具有摄像功能的手机已经成为一种便捷的二维条码阅读器,但手机因为本身的限制造成拍摄图像模糊,影响了后续的条码识别。本文提出了一种适合在手机平台上的图像盲恢复方法,以QR码识别为应用目的,利用QR码图像的灰度分布特点,通过模糊图像的边缘响应计算得到PSF函数,再进行图像恢复。实验证明,本文提出的方法具有去模糊效果好、计算简单的特点,适合手机等嵌入式平台的实时应用。  相似文献
10.
基于运动检测的图像去模糊算法   总被引:1,自引:0,他引:1  
针对相机曝光期间,由于相机和被拍摄物体之间存在相对运动而产生的图像模糊的问题,提出一种基于运动检测的图像去模糊算法。本算法根据相机成像的数学模型,推导出模糊核的参数(长度和方向)与相对运动之间的定量关系,通过维纳滤波对图像进行滤波去模糊。通过实验,可以观察到滤波过后的图像在细节上更加清晰,表明本方法能够一定程度上地去除模糊。  相似文献
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