基于双域滤波算法的OCT图像去噪

光学相干断层扫描(OCT)作为眼科疾病的重要诊断手段之一,具有巨大的发展潜力。OCT图像中含有大量的散斑噪声,会影响图像病理信息的检测和提取。提出一种去除OCT图像散斑噪声的方法,依据散斑噪声的乘性特性建立了乘性噪声模型。针对双域滤波中产生的伪吉布斯现象,引入各向异性扩散获得引导图像,代替了算法中的自身引导,减少了迭代次数。结果显示,所提方法提高了算法效率与图像质量,算法运行时间大幅度下降,图像峰值信噪比(PSNR)提高到36dB,结构相似性(SSIM)和边缘保持系数(EPI)分别达到0.8和0.7左右,有效抑制了OCT图像中的散斑噪声。     

传声器阵列特征值滤波去噪方法

作为二阶统计量的互谱矩阵(CSM)是声学成像算法的核心输入量。为增强传声器阵列的去噪表现,研究了互谱矩阵特征值滤波的机理,并提出了两种新型的特征值滤波方法的设计准则:(1)声源互谱矩阵的Stein无偏风险估计(SURE收缩),即基于SURE准则的特征值软阈值收缩;(2)进一步提高声源互谱矩阵EYM (Eckart-Young-Mirsky)估计误差的最优收缩(Opt-Shrinkage)方法,即对声源特征值所含有的噪声进一步去除。然后,在3000个快照数,0 dB信噪比(SNR),100个传声器的环境下进行仿真。通过仿真,与经典的MUSIC方法比较原声源信号与去噪后信号的互谱矩阵对角线误差,其中MUSIC方法的互谱矩阵对角线误差为74.15%,SURE收缩为41.97%,最优收缩为20.62%。并通过改变快照数、声源数与SNR,对比了不同特征值滤波去噪方法的效果。从仿真结果上看,在少声源(少于40个)工况下SURE收缩方法的去噪效果优于MUSIC方法与最优收缩方法;在声源数超过42个后,最优收缩方法取得更显著的效果。最后,在3个声源、60个传声器数、-5 dB信噪比的声源定位实验中,SURE收缩与最优收缩均获得了相比MUSIC方法更好的去噪效果。研究表明在现有传声器阵列特征值滤波方法的基础上,对特征值的进一步处理可以得到更好的去噪效果。     

全变分引导的双边滤波图像去噪方法

双边滤波是一种结合了图像空间邻近度和像素值相似度的非线性滤波方法。当噪声水平较小时,使用输入的待去噪图像来引导双边滤波核函数权重的计算较为可行,但当噪声水平变大时,噪声图像的结构信息被严重破坏,影响了双边滤波器中值域核函数权值的准确计算。提出了全变分引导的双边滤波图像去噪方法,给出其Split Bregman快速算法。利用全变分模型对噪声图像进行光滑,得到一幅包含清晰结构信息的图像,将图像作为双边滤波的引导图像进行计算,对上述过程进行迭代处理以提高算法的稳定性。实验表明,提出得算法不仅在主观视觉效果上优于双边滤波,客观评价指标PSNR值也较双边滤波提高了1.5dB左右;在较好去除噪声的同时较好地保持了边缘结构。     

阶跃型三维形貌信息小波去噪方法研究

阶跃样品显微测量时,样品三维形貌本身丰富的阶跃信息极易受到噪声高频信号的干扰,如何在滤除噪声的同时保持三维形貌的阶跃特征,实现对样品表面三维形貌信息的高精度测量是一个重要研究问题。利用小波函数良好的空间域和频率域的局部化特性,针对阶跃型样品的特点选取Haar小波,并采用一种基于模平方的阈值处理方法对三维形貌信息小波去噪方法进行研究。将该方法应用在本课题组研制的激光差动共焦显微镜扫描台阶样品得到的三维高度轮廓中,去噪后测量样品高度与OLYMPUS共焦显微镜扫描结果相对比,误差为0.146 8 nm,满足三维形貌信息后续测量分析的要求,证明了算法的有效性。     

混沌信号自适应协同滤波去噪

混沌信号协同滤波去噪算法充分利用了混沌信号的自相似结构特征,具有良好的信噪比提升性能.针对该算法的滤波参数优化问题,考虑到最优滤波参数的选取受到信号特征、采样频率和噪声水平的影响,为提高该算法的自适应性使其更符合实际应用需求,基于排列熵提出一种滤波参数自动优化准则.依据不同噪声水平的混沌信号排列熵的不同,首先选取不同滤波参数对含噪混沌信号进行去噪,然后计算各滤波参数对应重构信号的排列熵,最后通过比较各重构信号的排列熵,选取排列熵最小的重构信号对应的滤波参数为最优滤波参数,实现滤波参数的优化.分析了不同信号特征、采样频率和噪声水平情况下滤波参数的选取规律.仿真结果表明,该参数优化准则能在不同条件下对滤波参数进行有效的自动最优化,提高了混沌信号协同滤波去噪算法的自适应性.     

小波包节点分段阈值降噪在水声监听中的应用*

水声目标信号在发送、传播过程中,易受到环境噪声、系统自噪声等影响,因此水声监听过程中目标信号会掺杂大量噪声信息。为提高获取目标信号的准确性和可靠性,降低噪声,在已有小波分析基础上,提出小波包节点相对能量判断最优分解层,最优分解层节点系数分段阈值处理重构方法,实现水声监听信号分频段去噪。将0.1 kHz~8.4 k Hz实验数据按节点频率排序划分为5个强弱不同的频段信号实现消噪提取,结果表明该方法可将噪声信号与目标信号有效分离,与全局单一阈值相比,具有较好降噪能力。该方法打破了小波阈值去噪高频处理的局限性,提高了识别精度,改善了全局单一阈值去噪存在的短板,在鱼类分析识别、舰船监听、深海探测等方面具有一定的推广和应用价值。     

双向血流超声多普勒信号的小波降噪

利用小波框架结合软阈值的方法对表征双向血流的超声多普勒信号进行降噪。为了避免软阈值非线性处理对双向血流多普勒信号方向表征的影响,先对超声多普勒信号进行方向分离,然后对分离后的信号分别进行小波降噪,最后合成降噪后的双向血流超声多普勒信号。对计算机仿真的股动脉超声多普勒血流信号的降噪实验结果分析表明:本方法在不影响双向血流信息的前提下,提高了多普勒信号的信噪比、最大频率曲线和平均频率曲线的估计精度。仿真实验的结果证明了本方法可以用于双向血流超声多普勒信号的降噪。     

Wavelet and harmonic filtering for distortion-invariant volume holographic correlation

The wavelet and harmonic filtering method suggested by Zalevsky and Ouzieli is introduced in this paper and adopted in our volume holographic image recognition system. This composite filter combines several scaled versions of the cascaded wavelet and harmonic filter, obtaining high discrimination ability and wide dynamic range of rotation and scale deformations. Optical experiments are conducted to demonstrate the validity and practicability of the algorithm. To the best of our knowledge, this is the first report of using this algorithm in a volume holographic system. Moreover, the separate correlation approach proposed in this paper greatly simplifies the manufacturing process and reduces the cost of the system.     

一种基于时域滤波的红外序列图像去噪算法

红外图像是现代光学设备常用的图像源,图像显示效果直接影响设备的用户体验,而红外序列图像中的噪声会导致显示效果的下降.为了减轻噪声对红外序列图像显示效果的影响,通过历史多帧灰度值的加权和当前帧无噪声图像的估计,提出了一种基于时域高斯滤波的去噪方法.参考空域双边滤波的权值分配方法,引入了灰度值的影响对时域高斯滤波的权值进行...     

Real-time non-linear spatial filtering with a leaky OASLM

In this paper we demonstrate image processing techniques such as edge enhancement and phase contrast by using an optically addressed liquid crystal spatial light modulator (OASLM) in the frequency plane of an optical 4f processor. The transfer function of the device is derived on the basis of the Jones formalism. Faced with a lack of a general theory for such non-linear optical processors, we show that an analogy to the propagation of optical pulses in fibres is helpful for the understanding of the image processing operation.     

MRI denoising using progressively distribution-based neural network

Magnetic Resonance (MR) images often suffer from noise pollution during image acquisition and transmission, which limits the accuracy of quantitative measurements from the data. Noise in magnitude MR images is usually governed by Rician distribution, due to the existence of uncorrelated Gaussian noise with zero-mean and equal variance in both the real and imaginary parts of the complex K-space data. Different from the existing MRI denoising methods that utilizing the spatial neighbor information around the pixels or patches, this work turns to capture the pixel-level distribution information by means of supervised network learning. A progressive network learning strategy is proposed via fitting the distribution of pixel-level and feature-level intensities. The proposed network consists of two residual blocks, one is used for fitting pixel domain without batch normalization layer and another one is applied for matching feature domain with batch normalization layer. Experimental results under synthetic, complex-valued and clinical MR brain images demonstrate great potential of the proposed network with substantially improved quantitative measures and visual inspections.     

A shock-capturing methodology based on adaptative spatial filtering for high-order non-linear computations

A shock-capturing methodology is developed for non-linear computations using low-dissipation schemes and centered finite differences. It consists in applying an adaptative second-order filtering to handle discontinuities in combination with a background selective filtering to remove grid-to-grid oscillations. The shock-capturing filtering is written in its conservative form, and its magnitude is determined dynamically from the flow solutions. A shock-detection procedure based on a Jameson-like shock sensor is derived so as to apply the shock-capturing filtering only around shocks. A second-order filter with reduced errors in the Fourier space with respect to the standard second-order filter is also designed. Linear and non-linear 1D and 2D problems are solved to show that the methodology is capable of capturing shocks without providing dissipation outside shocks. The shock detection allows in particular to distinguish shocks from linear waves, and from vortices when it is performed from dilatation rather than from pressure. Finally the methodology is simple to implement and reasonable in terms of computational cost.     

小波变换域中图像噪声平滑技术

本文分析了小波变换应用于图像噪声平滑的原理，并对在应用中的小波函数的选择、信息剪裁和信号重构等几个重要问题进行了详细论述，对小波变换的图像滤波技术既能平滑噪声又能保留图像边缘的问题提出了具体要求。     

小波包分解域的超声图像非同态滤波降噪算法

针对医学超声图像中固有的斑点噪声,提出了一种基于小波包变换和非同态滤波的超声图像降噪算法。通过对小波包子带系数的邻域相关性进行研究,求解子带系数的归一化自协方差函数进而得到独立于小波包系数的邻域算子,提高了算法对信号和噪声的分离能力。然后对邻域算子和小波包系数进行最小均方误差联合估计得到非同态滤波降噪方程,实现对超声图像中斑点噪声的抑制。和传统医学超声图像降噪算法相比,该算法避免了对噪声的分布作近似处理和同态滤波过程,提高了算法的有效性。仿真和临床超声图像的实验结果征实,该算法不但能更有效的对斑点噪声进行抑制,也更好的保留了图像的细节信息。     

Complex denoising of MR data via wavelet analysis: application for functional MRI

A fast post-processing method for noise reduction of MR images, termed complex-denoising, is presented. The method is based on shrinking noisy discrete wavelet transform coefficients via thresholding, and it can be used for any MRI data-set with no need for high power computers. Unlike previous wavelet application to MR images, the denoising algorithm is applied, separately, to the two orthogonal sets of the complex MR image. The norm of the combined data are used to construct the image. With this method, signal-noise decoupling and Gaussian white noise assumptions used in the wavelet noise suppression scheme, are better fulfilled. The performance of the method is tested by carrying out a qualitative and quantitative comparison of a single-average image, complex-denoised image, multiple-average images, and a magnitude-denoised image, of a standard phantom. The comparison shows that the complex-denoising scheme improves the signal-to-noise and contrast-to-noise ratios more than the magnitude-denoising scheme, particularly in low SNR regions. To demonstrate the method strength, it is applied to fMRI data of somatosensory rat stimulation. It is shown that the activation area in a cross-correlation analysis is approximately 63% larger in the complex-denoised versus original data sets when equal threshold value is used. Application of the method of Principal Component Analysis to the complex-denoised, magnitude-denoised, and original data sets results in a similar but higher variance of the first few principal components obtained from the former data set as compared to those obtained from the later two sets.     

The effect of spatial filtering on non-linear distortions of laser diodes

It is demonstrated for the first time that spatial filtering of partially reflected irradiance of light from a directly modulated semiconductor laser diode produces a weak but appreciable amount of harmonic distortions. The laser diode discussed here is assembled to be misaligned in such a way that there is significant reflection of light from the submount attached to the heat sink. This geometry of laser and submount gives rise to interfered light intensities of simultaneous AM- and FM-modulated outputs in the far-field region. Numerical calculations are presented for a variety of laser structures and FM-modulation parameters. The second-order harmonic distortion is found to disclose multiple peaks with respect to the aperture radius of a receiving optical system.     

Consistency proof of two denoising methods and the parameter selection of PDE filtering method for ESPI

Electronic speckle pattern interferometry （ESPI） is a nondestructive, whole-field optical measurement technique. The removal of speckle noise is fundamental to extract measurement information accurately. In this letter, two filtering methods based on the oriented feature of ESPI fringes, i.e., the second-order oriented partial differential equation （SOOPDE） and oriented, regularized quadratic-cost function filtering methods, are first proven to be consistent. An important question in solving partial differential equation, i.e., how to select suitable parameters in an adaptive manner, is then discussed. The computer-simulated and experimentally obtained ESPI fringe patterns and phase map are processed by the SOOPDE filtering model with adaptive selective parameters. The qualitative and quantitative analyses demonstrate that the parameters selected by the adaptive method are effective and suitable for the SOOPDE filtering model.     

基于边缘软判决的小波域自适应图像去噪

提出了一个新的图像去噪方法。该方法基于非抽样小波变换的多分辨分解,在各尺度下对小波系数进行了边缘和非边缘分类,并根据它们的不同统计特性运用了不同的估计技术。鉴于边缘分类的不确定性,提出了依概率的软分类技术,通过计算边缘发生的概率,判决当前系数应该采用哪一种估计。仿真结果表明:该方法在滤除图像噪声的同时,边缘得到了保持,较目前存在的一些方法更具有优越性。