一种改进的三维块匹配图像去噪算法

目的为了有效消除噪声图像中的椒盐噪声、高斯噪声甚至混合噪声,改进三维块匹配算法,提出一种新的图像去噪算法。方法首先,该算法将含噪声图像用图像块之间的相似性构建三维矩阵。然后,在图像块之间进行硬阈值滤波降低噪声,对图像块集合加权平均重建得到初步估计去噪图像。最后,对初步估计结果图像进行块匹配,在图像块内和图像块之间进行维纳滤波和加权中值滤波,得到最终去噪图像。结果仿真结果表明,该算法对图像采集的常见噪声均表现出理想的去噪效果,PSNR值均大于31 d B。对比维纳滤波、中值滤波、硬阈值小波滤波,文中算法对高斯噪声、椒盐噪声和混合噪声的去噪结果 PSNR值为31.5334~36.6466 d B,均高于其他算法,最高差值达到12.08 d B。结论结合中值滤波和三维块匹配算法的图像去噪算法,能够较好去除噪声图像的多种类型噪声,是一种较为优秀的去噪算法。     

一种新的条码图像去噪方法

针对条码图像处理中去噪问题,提出一种新的基于边缘检测技术下的小波阈值去噪方法,在去除噪声同时考虑与高频噪声分量相似的条码边缘信息,不仅达到去噪的目的,而且可以有效地保存了条码的边缘信息.实验表明该方法达到令人满意的效果.     

一种结合小波变换和维纳滤波的图像去噪算法

目的为了有效消除噪声图像中的椒盐噪声、高斯噪声甚至混合噪声,结合维纳滤波的优势和小波分解各分量的特点,提出一种新的图像去噪算法。方法该算法先将含噪声图像进行小波变换,分离出1个低频分量和3个中高频分量,然后对低频分量进行自适应维纳滤波,对3个中高频分量用Canny算子提取边缘,最后将处理后的4个分量进行重构得到去噪后的图像。结果仿真结果表明,该算法对扫描仪引入的常见噪声均表现出较好的去噪效果,PSNR值均大于20 d B。尤其是对于高斯噪声和混合噪声,新算法去噪后的PSNR结果高于维纳滤波、软阈值小波滤波和文献[9]算法1~8 d B,效果较好。结论结合小波变换和维纳滤波的图像去噪算法,能够较好去除噪声图像的多种类型噪声,是一种较为优秀的去噪算法。     

图像去噪在印刷品质量检测上的应用

目的研究消除线阵CCD在采集图像过程中引入的噪声。方法根据印刷品检测系统中常引入高斯噪声和椒盐噪声结果,分别应用小波变换算法和高斯-中值混合算法进行去噪处理。结果通过与不同方法的对比分析,表明该2种方法可行。结论小波变换对高斯噪声图像能达到很理想的去噪效果,高斯-中值混合算法实现了在去噪的情况下还保持图像本身的细节部分,对椒盐噪声去噪效果明显。     

小波变换和大津法在竹编工艺中的应用

本文提出了一种新的竹编工艺,采用了数字图像处理算法,通过小波变换对扫描图等含噪图像进行滤波去噪,通过Ostu算法对图像进行黑白二值处理,从而就可以把任何一幅数字图案编织成竹编工艺品.为了去除图像中包含的大量噪声,提高基于图形处理的产品加工精度和效率,分析了维纳滤波对于图像消除噪声的特点,采用小波自适应阈值去噪技术剔除图...     

自适应小波阈值去噪算法在低空飞行声目标的应用EI北大核心CSCD

近年来,低空飞行声目标的探测与识别已得到军事领域的重点关注,而如何滤除信号中的背景噪声并准确保留信号的有效特征信息是该领域的一个难点。在研究小波去噪算法特点的基础上,针对低空飞行声目标信号的噪声特性,构建了一个新的阈值函数,通过自适应调整阈值函数实现在小波分解细尺度和宽尺度上对噪声信号最大限度的滤除,同时,运用香农熵理论来判断最优层数。通过大量的实验仿真验证,并与传统阈值去噪算法比较分析,结果表明该算法对去噪指标SNR有较大尺度的提高,可以更好的去除噪声,并对低空声目标信号去噪有很好的去噪效果。     

自适应小波阈值去噪算法在低空飞行声目标的应用

近年来,低空飞行声目标的探测与识别已得到军事领域的重点关注,而如何滤除信号中的背景噪声并准确保留信号的有效特征信息是该领域的一个难点。在研究小波去噪算法特点的基础上,针对低空飞行声目标信号的噪声特性,构建了一个新的阈值函数,通过自适应调整阈值函数实现在小波分解细尺度和宽尺度上对噪声信号最大限度的滤除,同时,运用香农熵理论来判断最优层数。通过大量的实验仿真验证,并与传统阈值去噪算法比较分析,结果表明该算法对去噪指标SNR有较大尺度的提高,可以更好的去除噪声,并对低空声目标信号去噪有很好的去噪效果。     

一种椒盐噪声点的检测算法技术研究

针对中值滤波算法虽然能够在很大程度上改善噪声带来的影响,但是使图像边缘变得模糊这一问题,提出了一种检测椒盐噪声的新算法。使用该算法对图像进行噪声检测后再对噪声点进行中值滤波能够有效地去除椒盐噪声。实验证明该算法具有明显的去噪能力,且当椒盐噪声比较严重时,也能够取得较好的滤波效果。     

一种基于小波变换的图像自适应去噪新方法

针对图像在日常拍摄和传输过程中极易受到干扰,造成图像模糊,分辨困难的问题。文章中首先对利用滤波去除图像噪声的方法以及利用常见的小波系数变换去除图像噪声的方法进行了说明并给出了小波变换去噪的步骤和流程图,进而针对性的提出了一种基于小波变换的自适应图像去噪方法。利用信噪比（SNR）和峰值信噪比（PSNR）两种客观评价方法将文章方法与其他方法进行了比较,数值仿真结果表明文章中提到的方法大大提高了图像去噪的水平,达到了自适应去噪的效果,说明该方法具有很好的实用性。     

基于低秩矩阵近似的低剂量CT图像两步去噪方法

低剂量CT扫描技术能有效减少患者所受到的辐射,但同时也导致了图像质量降低,尤其是图像中的条形噪声,给去噪工作带来了不小的挑战。针对这个问题,提出了一种基于低秩矩阵近似的低剂量CT图像去噪方法。该方法将低剂量CT图像的去噪过程分为两个阶段：第一阶段,利用图像结构信息的低秩特性,使用加权核范数最小化方法提取图像结构信息,实现对弱条形伪影和没有方向性的斑点噪声的去除;第二阶段,利用残留强条形伪影的旋转极低秩特性,即旋转后条纹噪声具有比背景内容更低的秩,再次利用低秩方法引入新的正则项,实现条纹噪声的去除。实验结果表明,该算法能够有效去除低剂量CT图像的条形噪声,并且较好地保留图像细节。     

Improving the response of a wheel speed sensor by using frequency-domain adaptive filtering

In this paper, a frequency-domain least-mean-square adaptive filter is used to cancel noise in a wheel speed sensor embedded in a car under performance tests. In this case the relevant signal is buried in a broad-band noise background, where we have little or no prior knowledge of the signal or noise characteristics. The results of the experiments show that the signal of interest and the noise (all forms of interference, deterministic, as well as stochastic) share the same frequency band and that the filter used significantly reduced the noise corrupting the information from the sensor while it left the true signal unchanged from a practical point of view. In this paper, a signal-to-noise ratio improvement higher than 40 dB is achieved. The results of the experiment show the importance of using digital signal processing when dealing with a signal corrupted by noise.     

数字聚焦超声反射CT成像初步研究

对于单元发，阵接收的模型，采用反射回波的包络作为投影，按滤波后投影算法（ＦＢＰ法）进行重建，得到物体内某一截面的重建像，然后调整接收阵的位置，获取多层截面的投影并重建成像，运用体绘制法（ＶｏｌｕｍｅＲｅｎｄｅｒｉｎｇＩｍａｇｉｎｇ）绘制了三维像，图像效果不好，分辨率不高，针对这一问题，文中应用了电子延迟方法处理接收信号，即对接收阵各阵元接收到的回波号依照散射点相对接收中阵的空间位置关系进行一定时间     

Noise reduction from two frame speckle-shifting ghost images with morphology algorithms

Edge detection is the basis of image segmentation and object recognition, as edge generally contains important information of an object. In this paper, we propose a novel speckle-shifting ghost imaging (SSGI) method to extract the edge of an unknown object. In this method, the gradient operation is directly carried out to the illumination patterns rather than the captured object image. The structured patterns for illumination are only divided into two groups, which can extract the edge in all directions. The imaging result is clearer than the conventional SSGI, but the noise is still serious. To solve the problem, we further investigate a denoising method with morphology algorithms, such as frame difference and connected region labelling. Numerical simulations and experiments are carried out to verify the feasibility and effectiveness.     

脉冲调制下相噪测量的环路设计和定标方法研究

在窄脉冲调制下相位噪声测量的定标过程中,通过采用PRF滤波器提取主谱线技术,对主载频差拍频率的波形进行了还原,使定标过程较之传统方法简便快捷。在脉冲调制状态下的锁相环路设计中,采用PRF滤波器消除PRF谱线,避免锁相环路难以锁定或者偏锁,另外通过提高环路增益等方法解决由于窄脉冲调制造成的环路锁定能力降低。应用以上技术的脉冲调制波相位噪声现场测量装置分别以不同的环路带宽对不同占空比下的脉冲调制波源的相位噪声进行了测量,证明了上述方法的可行性。     

Magnetic resonance tissue quantification using optimal bSSFP pulse-sequence design

We propose a merit function for the expected contrast to noise ratio in tissue quantifications, and formulate a nonlinear, nonconvex semidefinite optimization problem to select locally-optimal balanced steady-state free precession (bSSFP) pulse-sequence design variables. The method could be applied to other pulse sequence types, arbitrary numbers of tissues, and numbers of images. To solve the problem we use a mixture of a grid search to get good starting points, and a sequential, semidefinite, trust-region method, where the subproblems contain only linear and semidefinite constraints. We give the results of numerical experiments for the case of three tissues and three, four or six images, in which we observe a better increase in contrast to noise than would be obtained by averaging the results of repeated experiments. As an illustration, we show how the pulse sequences designed numerically could be applied to the problem of quantifying intraluminal lipid deposits in the carotid artery.     

Zero-order noise suppression with various space-shifting manipulations of reconstructed images in digital holography

In this paper, we describe various numerical space-shifting manipulations of the reconstructed images to remove the dc noise in the reconstruction, in terms of the periodicity characteristics of images in digital holography. The theoretical interpretation on different reconstruction periods of the image and the dc noise is described in detail for the first time, to the best of our knowledge. It is related to CCD sampling periods or frequencies for the fringes and the dc term of a hologram. With the calculations of Hadamard product of two different spatially shifted images and subsequent extraction of the root of it, the dc noise can be suppressed effectively and a clear image with the original intensity contrast can be obtained at the center in the hologram reconstruction, particularly when the image and the dc noise are completely or partially superposed with each other. The experiments for both in-line and off-axis imaging cases show that all results are completely consistent with theoretical predictions.     

射线照相噪声（颗粒度）的理论模式与实验验证

在射线照相影像质量的三要素中,影象噪声（颗粒度）是先决性的。本文介绍射线底片上噪声的理论处理模式,并对工业上常用的Agfa D4和D7胶片,就计算求出的相关于底片黑度的噪声量值,与实验作了比较,两者数值颇为一致。     

Noise robust one-dimensional blind deconvolution of medical ultrasound images

Recently, several blind cepstral deconvolution methods for medical ultrasound images were compared experimentally. The results indicated that the generalized cepstrum or the complex cepstrum with phase unwrapping give the blind homomorphic deconvolution algorithms with the best performance. However, the frequency domain phase unwrapping for pulse estimation, which is an essential part of both methods, is sensitive to the sensor noise when the values of the spectrum are small due to the randomness of the tissue response. The noise introduces abrupt changes in the phase. The phase degradation due to the noise causes variable spatial and gray scale resolution in image sequences following deconvolution. This paper introduces a noise robust Bayesian phase unwrapping method using a noncausal Markov random chain model. The prior regularizing term accounts for the noise and smoothes the phase. The phase unwrapping is formulated as a least mean square optimization problem. The optimization is done noniteratively by solving a difference equation using the cosine transform. The resulting improvement in radial and lateral blind deconvolution is demonstrated on six short ultrasound image sequences recorded in vitro or in vivo.     

Wick型随机WBK浅水波方程的白噪声泛函解

WBK浅水波方程是数学物理中的重要方程之一,对其研究有重要意义。经典方法都是在确定状态下研究波方程的性质和精确求解。随机分析和白噪声理论的建立和发展为波方程的研究提供了新的内容、方法和工具,因此,研究随机状态下波方程就成为可能。本文就是研究随机状态下WBK浅水波方程的精确求解问题:在Kondratiev分布空间(S)-1中,利用Hermite变换和齐次平衡法研究Wick-型随机WBK浅水波方程的精确求解,给出其白噪声泛函解,并给出了该方程在系数F(t)取不同白噪声泛函时的几个例子。     

基于FPGA设计的图像增强预处理器

就如何提高复杂背景条件下低信噪比的小目标检测概率问题展开讨论,提出了用数字滤波方法改善图像质量,提高信噪比,达到抑制背景噪声,增强小目标的目的。针对高帧频电视系统的特点,应用现场可编程门阵列（FPGA）构造高速图像预处理器,完成数字电视的图像预处理的实时计算,进而为后续工作打下良好的基础。仿真和实验结果证明是可行的。