基于波形分析的二维条码识别

传统的条码识别方法是通过边缘检测来定位条码边界，对于被光学系统点扩展函数严重模糊的高密度条码图像，这种方法的性能急剧下降，为了克服点扩展函数造成的模糊，提出了一种基于波形分析的二维条码识别算法，先在图像上定位条码位置，并在条码中分割出单行码字符号的图像，然后分析条码波形，计算出点扩展函数的标准方差，根据点扩展函数，重建条码波形，最后为了提高对图像噪音的抗干扰性，根据波形峰点定位条码边界，实验结果表明，基于波形分析的识别算法具有优秀的性能，显著地提高了高密度条码的识别率，满足了实际使用的要求。     

二维条码图像反模糊算法研究

二维条码作为一种重要的自动识别技术,有着极其广泛的应用前景。有效地解决二维条码图像降质问题是其能够广泛应用的关键。实际应用中,图像模糊是常见的降质,给二维条码识别带来了困难。论文研究二维条码图像反模糊算法,首先应用傅里叶变化确定图像模糊类型;然后根据条码信号,获得模糊函数的参数;最后采用用基于迭代技术的算法复原条码图像。实验结果表明,论文设计的算法具有良好的性能和实时性。     

基于手机平台的Data Matrix 2维条码识别

传统的条码图像采集和识别是通过工业扫描枪。近年来,随着移动增值业务和3G技术的发展,2维条码在手机设备的应用中得到飞速发展。以Data Matrix为例,研究了基于嵌入式手机设备的2维条码识别技术。首先根据Data Matrix条码的特点,给出了一种基于链码跟踪和线段检测的快速Data Matrix检测算法。接着分析了条码信号经过点扩展函数卷积后的降质模型,并利用维纳滤波对条码信号进行反模糊滤波。最后,针对透视畸变的现象,设计了一种适合于嵌入式手机设备的快速反透视算法。实验结果表明,提出的识别算法具有优秀的性能,显著提高了条码的识别率,满足了实际使用的要求。     

基于傅立叶变换的二维条码识别

研究了基于傅立叶变换的二维条码识别技术 .首先探讨了二维条码的定位分割技术 ,即在条码中分割出单行码字符号的图象 ,分析条码信号经过点扩展函数卷积后的降质模型 ,并讨论条码信号的一阶导数和中点的性质 ,通过分析条码信号 ,提出了一种计算点扩展函数标准方差的算法 ;然后利用傅立叶变换对条码信号进行反模糊滤波 ;最后对复原的条码信号做差分处理 ,并采用边界强度直方图策略自适应地选取阈值滤去噪声导致的无效边界 .在边界强度直方图中 ,采用基于矩阈值选取的方法寻找最佳阈值 .实验结果表明 ,该识别算法具有优秀的性能 ,显著地提高了条码的识别率 ,满足了实际使用的要求     

基于FFT的迭代盲反卷积图像恢复算法研究

迭代盲反卷积方法是同时估计出清晰图像和点扩展函数。该文主要是实现一种基于快速傅立叶变换的迭代盲反卷积算法。     

基于中点检测的二维条码识别

条码边缘模糊会导致其识别率下降，本文提出了一种基于中点检测的识别算法，能有效地解决边缘模糊对条码识别的影响．文中以PDF417条码为例研究了基于中点检测的二维条码识别算法．首先定位出图像上的条码，然后再在条码中分割出单个码字符号图像．文中最后根据分割出来的单个码字符号图像着重讨论了基于中点检测的识别算法．实验结果表明基于中点检测的识别算法具有良好的性能，显著地提高了条码的识别率，满足了实际使用的要求．     

复杂背景运动模糊图像的盲复原算法研究

为了有效恢复具有复杂背景的运动模糊图像，提出一种基于正则化策略和共轭梯度优化迭代复原算法；同时为了辨识运动模糊图像的参数，又提出一种基于模糊图像做分自相关函数的点扩展函数辨识算法。为验证算法的有效性，在微机上对提出的算法与现有算法进行了对比实验，结果表明十分有效，也具有较强的鲁棒性。     

一种改进的图像迭代盲反卷积算法

针对传统迭代盲反卷积算法收敛速度慢、容易出现解模糊等问题,提出一种改进的图像迭代盲反卷积算法。利用动量矩求解图像的有限支持域,在支持域中使频率域和空间域交替迭代,从而实现图像的盲复原。仿真结果表明,与传统迭代盲反卷积算法和基于小波变换的盲反卷积算法相比,该算法的收敛速度较快,具有较好的图像恢复效果。     

一种消除遥感影像中大气紊流的快速算法

根据大气紊流的特点建立了使图像退化的点扩展函数(PSF)模型,简要说明了近年来所用的LRW算法、迭代盲去卷积算法,提出了基于自相关函数的维纳(Wiener)滤波算法对退化图像进行快速处理.图像复原实验的结果表明,基于自相关函数的维纳(Wiener)滤波算法优于LRW算法和迭代盲去卷积算法.     

复合的图像盲复原算法

当点扩展函数未知或不确知的情况下,从观察到的退化图像中恢复原始图像的过程称为图像盲复原。近年来,图像盲复原算法得到了广泛地研究。迭代盲解卷积在抑制噪声放大与保留图像边缘信息有很好的效果,但在不知道点扩展函数并有噪声的情况下并不能有效的去除噪声导致图像恢复效果很差。针对图像盲复原的特点,提出了一种复合算法,该算法有效地解决了迭代盲解卷积的去噪问题,最后通过实验验证了算法的可行性和有效性。     

标刻在金属零件上的二维条码数据提取方法

针对金属零件上二维条码光照分布不均、点扩散、对比度低与污染干扰等问题,提出一种基于原灰度图像小区域相邻模块对比提取二维条码数据的算法.首先通过峰度值排序法及模块区域微调法由粗到精定位每个二维条码模块位置,然后基于原灰度图像利用遗传算法提取二维条码的数据信息,得到最终的提取结果.与传统二维条码数据提取算法的实验结果证明,该算法对于复杂金属背景上的二维条码识读具有更高的可靠性.     

Blind image deconvolution using machine learning for three-dimensional microscopy

In this work, we propose a novel method for the regularization of blind deconvolution algorithms. The proposed method employs example-based machine learning techniques for modeling the space of point spread functions. During an iterative blind deconvolution process, a prior term attracts the point spread function estimates to the learned point spread function space. We demonstrate the usage of this regularizer within a Bayesian blind deconvolution framework and also integrate into the latter a method for noise reduction, thus creating a complete blind deconvolution method. The application of the proposed algorithm is demonstrated on synthetic and real-world three-dimensional images acquired by a wide-field fluorescence microscope, where the need for blind deconvolution algorithms is indispensable, yielding excellent results.     

Bar code waveform recognition using peak locations

Traditionally, zero crossings of the second derivative provide edge features for the classification of blurred waveforms. The accuracy of these edge features deteriorates in the case of severely blurred images. In this paper, a new feature is presented that is more resistant to the blurring process, the image, and waveform peaks. In addition, an estimate of the standard deviation σ of the blurring kernel is used to perform minor deblurring of the waveform. Statistical pattern recognition is used to classify the peaks as bar code characters. The noise tolerance of this recognition algorithm is increased by using an adaptive, histogram-based technique to remove the noise. In a bar code environment that requires a misclassification rate of less than one in a million, the recognition algorithm showed a 43% performance improvement over current commercial bar code reading equipment     

Improving Medical CT Image Blind Restoration Algorithm Based on Dictionary Learning by Alternating Direction Method of Multipliers

In this paper, the medical CT image blind restoration is translated into two sub problems, namely, image estimation based on dictionary learning and point spread function estimation. A blind restoration algorithm optimized by the alternating direction method of multipliers for medical CT images was proposed. At present, the existing methods of blind image restoration based on dictionary learning have the problem of low efficiency and precision. This paper aims to improve the effectiveness and accuracy of the algorithm and to improve the robustness of the algorithm. The local CT images are selected as training samples, and the K-SVD algorithm is used to construct the dictionary by iterative optimization, which is beneficial to improve the efficiency of the algorithm. Then, the orthogonal matching pursuit algorithm is employed to implement the dictionary update. Dictionary learning is accomplished by sparse representation of medical CT images. The alternating direction method of multipliers (ADMM) is used to solve the objective function and realize the local image restoration, so as to eliminate the influence of point spread function. Secondly, the local restoration image is used to estimate the point spread function, and the convex quadratic optimization method is used to solve the point spread function sub problems. Finally, the optimal estimation of point spread function is obtained by iterative method, and the global sharp image is obtained by the alternating direction method of multipliers. Experimental results show that, compared with the traditional adaptive dictionary restoration algorithm, the new algorithm improves the objective image quality metrics, such as peak signal to noise ratio, structural similarity, and universal image quality index. The new algorithm optimizes the restoration effect, improves the robustness of noise immunity and improves the computing efficiency.     

基于游程编码思想的条码定位与识别方法

在复杂背景中的定位条码是图像式条码识别系统中的一个关键步骤,如何在复杂的背景中快速、白动地检测出条形码是该文的主要研究内容。首先介绍了条型码的编码结构,然后提出了一种基于游程编码思想的条码定位与识别方法,最后通过给出相关实验结果,验证了该算法的可行性和实用性。     

Blind image deconvolution via dispersion minimization

In linear image restoration, the point spread function of the degrading system is assumed known even though this information is usually not available in real applications. As a result, both blur identification and image restoration must be performed from the observed noisy blurred image. This paper presents a computationally simple iterative blind image deconvolution method which is based on non-linear adaptive filtering. The new method is applicable to minimum as well as mixed phase blurs. The noisy blurred image is assumed to be the output of a two-dimensional linear shift-invariant system with an unknown point spread function contaminated by an additive noise. The method passes the noisy blurred image through a two-dimensional finite impulse response adaptive filter whose parameters are updated by minimizing the dispersion. When convergence occurs, the adaptive filter provides an approximate inverse of the point spread function. Moreover, its output is an estimate of the unobserved true image. Experimental results are provided.