基于改进型右边正则度分布序列的低密度纠删码

通过对右边正则度分布序列的详细分析之后，给出了一种改进型右边正则度分布序列．证明了基于改进型右边正则度分布序列的级联型低密度纠删码能以任意接近删除信道容量的速率进行传输．同时指出所构造的级联型低密度纠删码的码率等于给定的码率，从而克服了基于原来的右边正则度分布序列的级联型低密度纠删码只能通过增大二部图右边结点的度数使得所构造纠删码的码率逼近给定的码率这一缺点．模拟结果验证了改进型右边正则度分布序列的正确性．     

一种不需噪声能量信息的准最佳正则图像复原

提出一种基于正则化方法的高效图像复原技术。围绕最小化正则解模糊误差，设计该技术。利用泰勒级数定性地分析怎样的正则化算于使正则解模糊误差能量较小，得出结论：通常情况下应选取低阻高通的正则化算子；利用随机理论解决正则解模糊误差能量期望值最小化问题，确定正则化参数；利用小波变换估计噪声能量，在没有噪声能量信息的情况下，新方法能进行高效的图像恢复。实验结果表明本文的恢复技术比传统方法的恢复性能好，恢复效果接近最佳且性能稳定，且不需要噪声能量信息。     

一种图象恢复效果接近最优的正则化方法

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．     

图象重建中的有理逼近方法

提出一个由不完备投影数据重建图象的有理逼近方法。该方法首先引入图象象素间连接的假设,然后导出满足最优解的线性方程组,再通过在此方程组中引入一个人工参数将其变形,并用摄动展开方法解新形成的线性方程组,再利用向量值函数的有理逼近来构造原线性方程组的解。该方法还避免了原方程组直接求解计算量非常大的问题,因为使用该有理逼近方法,只需展开几项,便可获得较满意的重建图象。     

一种用于LTE终端的粒子群加权重构定位算法

为解决LTE终端在NLOS环境下定位精度较低的问题,通过加权重构定位矩阵并引入残差,将其转化为权值寻优的问题,再利用改进型粒子群算法进行权值寻优,以消除NLOS噪声带来的误差。该方案由于采用了线性改变惯性权重的方式,能有效提升寻优的效率;同时,通过逐代保存重构权值可逐步消除NLOS误差,进而提升定位的精度。仿真结果表明,相对于chan算法和改进型taylor算法,该算法能快速逼近最优解,在不同NLOS环境下定位误差减少量超过13%。     

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。     

图形与图象再现的有限元算法

在计算机视觉以及CAD／CAM等领域都涉及到基于离散观测数据的目标图象或三维曲面图形的重建问题，为此，将最佳逼近与数据平滑理论相结合，提出了一种基于有限元技术的图形与图象重建方法，该方法首先采用Lagrange乘子方法建立正定泛函，进而应用有限元技术及Wewton失代方法求得函极值解，然后通过有限元解的插值计算，重新构造出图形或图象，由于结合了数据平滑处理，因此该方法不仅消除了数据上噪声的影响，而且提高了重建的精度，实验结果证明了方法的有效性和实用性。     

基于区域的三叉树图象编码新方法

本文提出了一种基于区域的图象编码新技术，它根据图象区域灰度分布特点，以灰度误差最小平方和为准则，采用四向递归二分法逐渐将图象表面划分为若干凸多边形，使之逼近原始图象。软件模拟实验表明，当头肩灰度图象压缩比超过２０：１时，重建图象主观质量仍然较好。文中介绍了四向递归二分法的基本算法和二叉树图象编码方法，同时给出了若干实验结果。     

基于区域的二叉树图象编码新方法

本文提出了一种基于区域的图象编码新技术，它根据图象区域灰度分布特点，以灰度误差最小平方和为准则，采用四向递归二分法逐渐图象表面划分为若干凸多边形，使之逼近原始图象。软件模拟实验表明，当头肩灰 度图象压缩比超过２０：１时，重重建图象主以质量仍然较好。文中介绍了四向递归二分法的基本算法和一叉树图象编码方法，同时给若干实验结果。     

基于简化GEVD技术的椭圆拟合算法

传统的椭圆拟合使用标准广义特征值(GEVD)分析算法.通过统计分析技术,可知该技术在拟合数字椭圆时,存在估计偏差大、均方误差大的缺点.其产生原因是数据噪声的有色性和自相关函数矩阵的条件数过大,因此对数据噪声的预白化滤波和对数据的正则化变换是提高椭圆拟合的有效措施.这从理论上有力支持了Hartley提出的正则化技术.根据分析,我们开发了一个简化GEVD技术.通过理论分析和计算机仿真实验,表明了它固有地同时具备噪声预白化功能和数据正则化功能,因此,它能给出均方误差相当小的无偏估计,由于它无须进行预白化变换或正则化变换,并把求解GEVD过程的维数从6降为2,所以它还具有计算快速、实现简单方便的优点.     

Efficient implementation of image interpolation as an inverse problem

This paper presents three computationally efficient solutions for the image interpolation problem which are developed in a general framework. This framework is based on dealing with the problem as an inverse problem. Based on the observation model, our objective is to obtain a high resolution image which is as close as possible to the original high resolution image subject to certain constraints. In the first solution, a linear minimum mean square error (LMMSE) approach is suggested. The necessary assumptions required to reduce the computational complexity of the LMMSE solution are presented. The sensitivity of the LMMSE solution to these assumptions is studied. In the second solution, the concept of entropy maximization of the required high resolution image a priori is used. The implementation of the suggested maximum entropy solution as a single sparse matrix inversion is presented. Finally, the well-known regularization technique used in iterative nature in image interpolation and image restoration is revisited. An efficient sectioned implementation of regularized image interpolation, which avoids the large number of iterations required in the interactive technique, is presented. In our suggested regularized solution, the computational time is linearly proportional to the dimensions of the image to be interpolated and a single matrix inversion of moderate dimensions is required. This property allows its implementation in interpolating images of any dimensions which is a great problem in iterative techniques. The effect of the choice of the regularization parameter on the suggested regularized image interpolation solution is studied. The performance of all the above-mentioned solutions is compared to traditional polynomial based interpolation techniques such as cubic O-MOMS and to iterative interpolation as well. The suitability of each solution to interpolating different images is also studied.     

Multiplicative noise removal via combining total variation and wavelet frame

Total variation (TV) regularization has been proved effective for cartoon images restoration however it produces staircase effects, and properly wavelet frames were confirmed to provide a more smoothing approximation to the original image. In this paper, a new model for multiplicative noise removal was proposed, which combines wavelet frame-based regularization and TV regularization. A modified proximal linearized alternating direction method is developed to solve the proposed model, considering that adding a new regularization term to the TV model would yield more parameters, which will result in computational difficulties. For the new model, the existence of solution and the convergence property of the proposed algorithm are proved. Numerical experiments have proved that the proposed model has a superior performance in terms of the peak signal-to-noise ratio and the relative error values for non-piecewise constant images when compared with some state-of-the-art multiplicative noise removal models.     

Planar Numerical Signature Theory Applied to Object Recognition

Geometric invariants play a crucial role in the field of object recognition where the objects of interest are affected by a group of transformations. However, designing robust algorithms that are tolerant to noise and image occlusion remains an open problem. In particular, numerical signature-invariants in terms of joint invariants, as an approximation to the differential signature-invariants, suffer instability, bias, noise and indeterminacy in the resulting signatures. This paper addresses some of these issues in respect of planar signatures. To improve the stability in the Euclidean case, we replace Heron’s formula by the “accurate area” and then we demonstrate that the proposed algorithm is, not only numerically stable but is also, in terms of mean square error, a closer approximation (by at least a factor of three) compared with the original formulation of Calabi. To reduce noise in the resulting curves “the n-difference technique” and “the m-mean signature method” are introduced and we show that these methods are capable of minimizing noise by more than 90 %. The n-difference technique can also be applied to eliminate indeterminacy in the outputs. For the equiaffine case, we improve and extend the required formulation for the implementation of Signature theory for any planar meshes with a general position property. Moreover, we introduce a general formulation for the full conic sections to determine an equiaffine-invariant numerical approximation to the equiaffine arc length, measured along the given curve between any two points of the mesh. Finally, we demonstrate the discriminative power of the concept of discrete signature analysis for distinguishing normal and abnormal regions in the medical imaging domain.     

一种基于遗传算法的数字曲线多边形逼近方法

提出了一种基于遗传算法的数字曲线多边形改进逼近方法。该方法针对规则形状数字曲线的多边形逼近问题,以二进制向量序列表示的染色体作为每一个对应的逼近多边形候选解,将简化前后多边形质心偏移误差以及各被替换线段欧氏距离的方差引入到适应函数中,用迭代次数的sigmoid函数作为变异概率来控制遗传算法优化求解过程中的全局和局部搜索特性。实验结果表明,该方法对于保持曲线多边形简化逼近后的形状特征具有较好的效果。     

噪声不敏感的柱状图特征描述符及其在图像检索中的应用

基于内容的图像检索方法往往将图片的内容表示成柱状图,根据图片柱状图之间的相似性进行图片的检索。数码图片中包含的噪声使得柱状图变得平滑,从而使图片之间变得更为相似,这增加了返回结果中包含的图片数量。为了进一步提高图片检索的性能,提出了一种对噪声不敏感的柱状图特征描述符,并应用该特征描述符进行图像之间的相似性匹配。首先将图片中的噪声描述为平稳附加高斯白噪声,并给出了相应的柱状图表示;然后通过随机变量的原点矩定义了柱状图的特征描述符,并分析了如何应用特征描述符恢复原始图片的柱状图。在算法的性能验证过程中,将提出的方法与4种相关算法进行比较,应用两个真实的图片数据库的图像检索实验验证了所提方法的有效性。     

Mutual-manifold regularized robust fast latent LRR for subspace recovery and learning

In this paper, we propose a simple yet effective low-rank representation (LRR) and subspace recovery model called mutual-manifold regularized robust fast latent LRR. Our model improves the representation ability and robustness from twofold. Specifically, our model is built on the Frobenius norm-based fast latent LRR decomposing given data into a principal feature part, a salient feature part and a sparse error, but improves it clearly by designing mutual-manifold regularization to encode, preserve and propagate local information between coefficients and salient features. The mutual-manifold regularization is defined by using the coefficients as the adaptive reconstruction weights for salient features and constructing a Laplacian matrix over salient features for the coefficients. Thus, some important local topology structure information can be propagated between them, which can make the discovered subspace structures and features potentially more accurate for the data representations. Besides, our approach also considers to improve the robust properties of subspace recovery against noise and sparse errors in coefficients, which is realized by decomposing original coefficients matrix into an error-corrected part and a sparse error part fitting noise in coefficients, and the recovered coefficients are then used for robust subspace recovery. Experimental results on several public databases demonstrate that our method can outperform other related algorithms.

     

改进的空间自适应规整化图像复原

在对空间自适应规整化图像复原算法的研究中发现,如果图像中混杂有椒盐噪声,则自适应算法的复原效果并不理想。针对这一问题,提出了一种新的可见度函数,用模糊熵代替均方差作为评价图像灰度值变化程度的判据,使得算法在提高复原图像质量的同时,能够适应更多类型噪声的干扰。仿真实验结果表明,在椒盐噪声存在的情况下,新算法的性能远优于原算法,对模糊-噪声图像的复原结果也在实验中给出。     

基于改进FOCUSS算法的电容层析成像算法研究

针对电容层析成像(electrical capacitance tomography,ECT)逆问题求解的病态性和不适定性,在压缩感知(compressed sensing,CS)的基础上,提出一种改进FOCUSS的ECT重建算法。采用离散余弦变换(DCT)基将原始图像灰度信号进行稀疏化处理,在使用正则化FOCUSS算法求解的过程中引入拟牛顿法逼近求解中间稀疏变量,以提高信号重构的准确性。仿真实验结果表明,同LBP、Tikhonov和Landweber和FOCUSS算法相比,改进的FOCUSS算法能够有效区分物场中的不同介质,改善图像过度平滑的问题,减小图像误差至0.23,提高图像相关系数至0.80,具有更好的成像效果,为ECT图像重建算法的研究提供新的思路。     

Double expanding robust image watermarking based on Spread Spectrum technique and BCH coding

This paper presents a new algorithm for blind image watermarking which has a high robustness against common image processing attacks such as noise addition (Gaussian noise, Salt & Pepper noise, Speckle noise and etc.), JPEG and JPEG2000 compressions, Histogram Equalization, Average and Gaussian filters, Scaling and Cropping. According to this fact that a watermark with about 70 bits is enough for copyright protection, consequently in this paper a small watermark (64 bits) have been double expanded into multi larger meaningful bits with applying BCH error correction code and Spread Spectrum technique in order to reduce errors in extraction phase. Approximation subband of two levels DWT transform is divided into non-overlapping blocks and high frequency coefficients of DCT transform of each block is used for embedding the watermark. Embedding technique, which is used in this paper, is Spread Spectrum. Correlation between some coefficients of each embedded block and two predefined groups of random bits is used for watermark extraction, so this method is blind and does not need to the original image or additional information in extraction phase. Another idea, which is used in this paper, is calculating different gain factors for each block of approximation subband according to the texture of each block. Consequently this method allocates smaller gain factors to smooth blocks and larger gain factors to texture and rough blocks. So, manipulating in image will be more robust and imperceptible.