基于光纤振动的激光散斑控制

散斑噪声的存在使得图像灰度剧烈变化,降低了图像分辨率,影响成像质量。为了控制散斑噪声,使用波长为405nm的激光作为显微系统照明光源,利用音圈电机振动光纤,通过对抛光玻璃显微成像,用CCD图像采集卡采集图像后进行了散斑噪声对比度分析。结果表明,在光纤振动幅度不变、振动频率在4Hz~55Hz内逐渐增加时,图像散斑对比度在0.0326~0.1197范围内逐渐变小;当频率大于51Hz时,图像散斑对比度曲线趋于平稳且对比度在0.0326处获得了最小值,图像清晰,达到良好的散斑控制。     

由统计特性分析激光主动成像系统图像的噪声性质

激光主动成像系统具有分辨率高、抗干扰能力强、能成三维像等一系列优点，因而应用越来越广泛，但它的图像同时受到散斑噪声和其他一些高斯噪声的影响。散斑噪声为一种乘性噪声，高斯噪声则为一种加性噪声，这两种噪声哪种占据主要地位对于图像噪声抑制噪声模型的建立具有重要的意义，分析了受到散斑噪声和高斯噪声污染图像的概率曲线及灰度均值方差比等统计特性，找出在两种噪声分别作用下的不同统计特性，以此为判别标准，对激光主动成像系统图像进行分析，得出了此图像所受噪声的主要成分为散斑噪声的结论。同时，算出了此图像的散斑噪声方差。     

多尺度压缩感知鬼成像系统

现有的鬼成像系统中都使用固定尺度的散斑进行成像,而目前的仪器可以实现多尺度散斑鬼成像。本文对采用多尺度散斑的压缩感知鬼成像系统进行研究。采用计算机仿真模拟的方法,利用具有四个分辨率尺度的图像作为原始图像,对多种不同尺度散斑且不同比重情况下的恢复图像进行了研究分析。结果表明采用多尺度散斑的压缩感知鬼成像系统可以得到更加靠近真实值的图像,且在小散斑尺寸占据大比重的情况下,获取图像的分辨率不会受到干扰。     

激光成像系统图像散斑抑制算法比较

CO2激光相干成像系统具有分辨率高、抗干扰能力强、能三维成像等特点,但其所成图像易受到散斑噪声的污染造成分辨率下降,因此研究适用于此成像系统图像散斑噪声抑制的算法具有重要的实用价值。文中对常用的噪声抑制算法:中值滤波、同态均值滤波和Lee滤波进行了介绍,并将其应用于CO2脉冲激光相干成像系统图像的散斑噪声抑制处理,比较了三种算法的处理结果。结果表明,采用小窗口、不迭代Lee滤波进行处理时,既明显抑制了散斑噪声,又有效保持了图像的边缘信息,所以用它进行激光成像系统图像散斑噪声抑制处理是合适的。     

激光主动探测图像散斑自适应滤波抑制方法研究

激光主动照明成像具有作用距离远、系统分辨率高、可在低照度背景等复杂环境下获取目标图像等优点,但探测图像会受散斑噪声干扰.把高斯滤波、均值滤波和自适应滤波方法分别应用到仿真实验中进行散斑噪声抑制,实验表明:与高斯滤波和均值滤波相比,自适应滤波能有效抑制图像噪声,保留图像的边缘和细节信息.利用自适应滤波方法对获取的单帧和多帧累加平均的激光主动探测图像进行散斑抑制实验,使用散斑对比度进行定量分析,结果表明多帧短曝光图像累加平均可有效抑制图像的散斑噪声,自适应滤波可进一步降低图像的散斑噪声.     

基于灰度共生矩阵的激光散斑评价方法

为了抑制激光成像中的散斑噪声,采用灰度共生矩阵构建了激光显微散斑分析评价系统。系统采用波长为405nm的激光作为显微系统光源,利用电压驱动音圈电机振动样品对激光散斑噪声大小进行控制,同时采用CCD图像传感器采集不同驱动电压下的激光散斑噪声。通过对散斑噪声的角二阶矩、对比度、熵和逆差距特征参量的分析,较好地表征了激光散斑噪声的变化。结果表明,灰度共生矩阵方法可对激光显微成像中的散斑进行评价。     

基于TV-维纳滤波的散斑噪声抑制

数字全息具备成像方便、分辨率高和实时观测等优势,已广泛应用于信息加密、显微观测、医学诊断、元器件测量等领域.但在数字全息成像中,散斑噪声严重影响了再现像的信噪比和成像分辨率,因此为了提高数字全息成像质量,迫切需要抑制再现像的散斑噪声.本文提出了一种基于TV模型和维纳滤波融合的散斑噪声抑制方法.仿真实验表明经该算法处理的再现像平滑效果好,去噪声能力强,和原图的相似度高.     

分数阶微分局部强反射的去噪方法应用

针对具有强反射的表面光条图像出现散斑或复合散斑等严重噪声情况,该文提出一种利用分数阶微分增强的图像去噪声的处理算法,突出噪声的颗粒化特征,通过连通区域面积统计的方法对有效连续光条进行分离并去除散斑噪声,获得有效光条图像,最后利用灰度重心法提取有效光条的中心。经实验对比,该方法得到的信息熵值和光条中心提取精度都显著提高,体现了分数阶微分算法增强图像高频信息的同时,有效保留更多的低频信息的特点,保留了更多的图像纹理细节,显著提高了特征光条中心提取精度。     

相位差异散斑成像技术验证实验

相位差异散斑成像综合了相位差异和散斑成像两种图像处理方法,相位差异同时采集已知相位差的离焦和在焦图像,联合恢复目标图像和解算波前相位,相位差异散斑成像是在相位差异的基础上,利用多帧短曝光图像组来解算出它们所对应的波前畸变序列,同时对图像进行恢复,每帧图像组由在焦和已知离焦量的两幅图像组成。为了实践相位差异散斑成像技术,分别在室内和室外做了相关实验,在室内利用变形镜模拟光学系统像差,利用精密位移台移动相机分时采集焦面和离焦面两幅图像,恢复后的图像分辨率提高12%;室外利用某望远镜光学系统,在其后放置分光棱镜将光束平均分给焦面和离焦面两台相机,外触发同时采集短曝光图像,对20 km外的目标成像,经过单帧和多帧图像的恢复,目标图像分辨率得到显著提高。实验结果证明:相位差异散斑成像技术可广泛应用于靶场光学设备测量和地基大口径望远镜的探测成像。     

激光片光三维传感中提高深度分辨率的方法

提出了两种在激光片光三维传感中降低散斑噪声、提高深度分辨率的方法。第1种方法采用宏观上相同、微观上受物体微结构面元影响的多帧散斑图像叠加,用于三维测量系统的标定。第2种方法利用动态采集方式,使CCD对微结构元进行时间积分,可有效地降低散斑噪声的影响。实验证明,综合利用这两种方式,深度分辨率可以得到有效的提高。     

Speckle noise removal based on structural convolutional neural networks with feature fusion for medical image

Application of convolutional neural networks (CNNs) for image additive white Gaussian noise (AWGN) removal has attracted considerable attentions with the rapid development of deep learning in recent years. However, the work of image multiplicative speckle noise removal is rarely done. Moreover, most of the existing speckle noise removal algorithms are based on traditional methods with human priori knowledge, which means that the parameters of the algorithms need to be set manually. Nowadays, deep learning methods show clear advantages on image feature extraction. Multiplicative speckle noise is very common in real life images, especially in medical images. In this paper, a novel neural network structure is proposed to recover noisy images with speckle noise. Our proposed method mainly consists of three subnetworks. One network is rough clean image estimate subnetwork. Another is subnetwork of noise estimation. The last one is an information fusion network based on U-Net and several convolutional layers. Different from the existing speckle denoising model based on the statistics of images, the proposed network model can handle speckle denoising of different noise levels with an end-to-end trainable model. Extensive experimental results on several test datasets clearly demonstrate the superior performance of our proposed network over state-of-the-arts in terms of quantitative metrics and visual quality.     

小波包域值法去除合成孔径雷达图像斑点噪声

合成孔径雷达(SAR)图像的斑点噪声使图像的解译能力明显降低,影响了SAR图像的应用,去除图像斑点噪声是SAR图像处理及应用的前提。去除的方法在图像域有很多,在频率域,也有基于FFT变换和小波变换的许多方法。文中根据沙漠地区的SAR图像的特点,对基于图像域和频率域的几种滤波方法进行了比较,提出了一种基于小波包变换的SAR图像斑点噪声去除方法,结果表明该方法既去除了斑点噪声,又保持了边缘特征信息。     

激光雷达主动偏振图像散斑抑制算法研究

针对激光主动偏振图像的散斑去除问题,提出了一种新的非局部正则化方法:根据激光主动偏振图像的噪声特点,在全变差模型的基础上,提出了非局部全变差正则化模型.该算法充分利用了图像的全局信息复原图像,在很好地抑制散斑的同时,保持了图像的细节信息.新模型使用轮流最小化方法进行求解,则原始图像和点扩散函数都可以在最小化框架中求解,...     

A convex variational method for super resolution of SAR image with speckle noise

Super resolution (SR) is an attractive issue in image processing. In the synthetic aperture radar (SAR) image, speckle noise is a crucial problem that is multiplicative. Therefore, numerous custom SR methods considering additive Gaussian noise cannot respond to this image degradation model. The main contribution of this paper is to propose a novel variational convex optimization model for the single SAR image SR reconstruction with speckle noise that is one of the first works in this field. Employing maximum a posteriori (MAP) estimator and proposing an effective regularization based on combination of sparse representation, total variation (TV) and a novel feature space based soft projection tool to use merits of them is the main idea. To solve the proposed model, the split Bregman algorithm is employed efficiently. Experimental results for the multiple synthetic and realistic SAR images show the effectiveness of proposed method in terms of both fidelity and visual perception.     

Efficient coding of experimental holograms using speckle denoising

Lossy compression for Experimental Holograms (EH) and Computer-Generated Holograms (CGH) using standardized coding solutions is a highly efficient process provided that these solutions can be applied to the object plane. This compression efficiency reveals to be more relevant in CGH. Speckle noise mainly affects reconstructed EH, and to less extent reconstructed CGH. In the current work, the reduction of speckle noise of EH is proposed to improve the coding efficiency of the hologram compression scheme. The compression scheme defines a base layer where a 2D version of the object is coded with an image codec standard. When speckle noise reduction is performed before any compression, efficient compression is obtained for both CGH and EH. Since speckle noise reduction is performed only on amplitude data, without affecting the phase information of the reconstructed hologram, it is still possible to render 3D features such as depth map, multi-view or to recover holographic interference patterns for further 3D visualization.     

A multiplicative Nakagami speckle reduction algorithm for ultrasound images

Speckle noise of ultrasound images is of multiplicative nature which degrades the image quality in terms of resolution and contrast. While there exist a number of algorithms for reduction of multiplicative Rayleigh distributed random speckle noise, the low signal-to-noise ratio (SNR) issue of the multiplicative Rayleigh noise is still not adequately resolved. In this paper, a simple 2-dimensional (2D) local intensity smoothing method is presented which transforms the Rayleigh noise contaminated in ultrasound images to Nakagami distributed noise so as to improve the SNR of processed images. A 2D total variation regularized Nakagami speckle reduction algorithm is derived based on the maximum a posteriori estimation framework, which performs well in restoring piecewise-smooth reflectivity and preserving fine details of the image. The proposed algorithm is verified by a series of computer-simulated and real ultrasound image data. It is shown that the algorithm considerably improves the quality of ultrasound images and outperforms the Rayleigh noise based speckle reduction methods in terms of speckle SNR and contrast-to-noise ratio.     

反射式离轴数字全息再现像中的噪声抑制

为了抑制反射式离轴数字全息再现像中的零级像和散斑噪声对再现像的影响,利用高斯核函数在空域对数字全息图进行低通滤波,再用原数字全息图减去滤波后的低频信息来抑制零级像,采用非局部均值滤波来抑制散斑噪声。实验结果表明空域高斯核函数处理法能有效地抑制零级像,非局部均值滤波可以抑制散斑噪声但对部分细节的保持能力不强,改进后的非局部均值滤波在抑制散斑噪声的同时能很好地保护细节信息,提高了离轴数字全息再现像的质量。     

Two-dimensional adaptive block Kalman filtering of SAR imagery

A method for removing speckle from synthetic aperture radar (SAR) imagery by using 2-D adaptive block Kalman filtering is introduced. The image process is represented by an autoregressive model with a nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which taken into account not only the effect of speckles as multiplicative noise but also the effects of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as those of the additive noise