Space/spatial-frequency analysis based filtering

Space-invariant filtering of signals that overlap with noise in both space and frequency can be inefficient. However, the signal and noise may be well separated in the joint space/spatial-frequency domain. Then, it is possible to benefit from the application of space/spatial frequency approaches. Processing based on these approaches can outperform space or frequency invariant-based methods. To this aim the concept of nonstationary space-varying filtering is introduced in this paper as an extension of the time-varying filtering concept. The filtering definitions are based on statistical averages, although the filtering should commonly be applied knowing only a single noisy signal realization. The procedures that can produce good estimates of quantities crucial for efficient filtering, based on a single noisy signal realization, are considered. Special attention has been paid to the region of support estimation and cross-term effects removal. The efficiency of the proposed space/spatial-frequency filtering concept is tested on the signal forms inspired by the interferograms in optics, including real images as disturbances. Examples demonstrate the superiority of the proposed filtering over the space-invariant one for the considered type of signals and noise     

New data processing method for improving the measurement accuracy in FTP

During the 3D shape measurement,there are noises in the images that are obtained by the capture system.The traditional method,Fourier transform profilometry(FTP) technique,improves the accuracy only by the filtering method in the frequency domain.In this paper,the curve fitting method is used for the light field distribution calculation before the filtering process applied in the frequency domain by choosing a suitable filter window,and then the higher quality of the basic frequency component signal is got.This method can avoid the frequency overlapping caused by the noise,so the improvement of the measuring accuracy of FTP is realized.     

医学图像增强系统设计

为了改善医学图像的视觉效果,提高图像的清晰度,使之更适合于机器的分析处理以及人的视觉特性,并突出病灶点,为病理学诊断和临床诊断提供可靠依据。设计了一个对医学图像十分具有针对性的图像增强系统。针对CT图像的电子噪声提出了基于修正维纳滤波的小波包去噪算法;针对B型超声图像的散斑噪声提出了基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法;针对医学图像对比度低,边缘信息模糊等特点,提出了基于小波变换的医学图像增强算法。当噪声方差为0.01时,基于脉冲耦合神经网络(PCNN)模型的小波自适应斑点噪声滤除算法获得的PSNR比经Wiener滤波方法获得的PSNR高出9 dB。系统能快速找到噪声点进行定点去噪,能有效提高医学图像的对比度,增强边缘细节信息,突出病灶点的位置,从而达到较好的处理效果,为医疗工作者观察病症提供更加清晰准确的依据。     

基于噪声检测的高密度椒盐噪声自适应滤波算法

为了减少图像中的椒盐噪声对后续图像处理的影响,针对高密度噪声污染图像,提出了基于噪声检测的高密度椒盐噪声滤波算法。噪声检测方法理论可靠,保证了较高的噪声检测率,根据噪声点邻域信号点分布的不同采用不同的策略,能最大限度的保护图像的细节信息,使得高密度噪声污染图像也能得到较好地恢复。实验结果表明,所提出的滤波算法具有较强的自适应性、较高的算法保真率及较好的滤波效果。     

Postprocessing of images by filtering the unmasked coding noise

This paper presents a methodology for the restoration of the visual quality of still images affected by coding noise. This quality restoration is achieved only by considering the additive coding noise and is therefore limited to an adaptive postprocessing filtering. It is based on a model of the human visual system that considers the relationship between visual stimuli and their visibility. This phenomenon known as masking is used as a criterion for the locally adaptive filtering design. An image transformation that yields visual stimuli tuned to the frequency and orientation according to the perceptual model is proposed. It allows a local measure of the masking of each perceptual stimulus considering the contrast between signal and estimated noise. This measure is obtained by analytic filtering. Processing schemes are presented with applications to the discrete cosine transform (DCT) and subband coded images. One proposed solution considers the characteristics of DCT coding noise for the estimation of the noise. Another solution is based on a "blind" neural estimation of the noise characteristics. Experimental results of the proposed approaches show significant improvements of the visual quality, which validates our perceptual model and filtering.     

基于加权光耦合阵列的任意波形产生

提出了基于加权光耦合阵列的光电任意波形产生方案,利用不同波长加权和并行耦合的方法实现信号光域的数模转换。通过对信号的频谱分析发现,多波加权存在拍频噪声,经过波长预设和滤波处理,可以将高频噪声有效滤除,但在信号交变的时刻会有尖峰干扰出现,进一步通过与变换信号带宽匹配的滤波平滑处理后,尖峰干扰得到一定的抑制,可实现输入信号速率为10 Gb/s、量化精度为5 bit的光学数模转换,实现了三角波、锯齿波、高斯脉冲和方波序列等光学波形的输出。     

Spatial domain filtering for fast modification of the tradeoff between image sharpness and pixel noise in computed tomography

In computed tomography (CT), selection of a convolution kernel determines the tradeoff between image sharpness and pixel noise. For certain clinical applications it is desirable to have two or more sets of images with different settings. So far, this typically requires reconstruction of several sets of images. We present an alternative approach using default reconstruction of sharp images and online filtering in the spatial domain allowing modification of the sharpness-noise tradeoff in real time. A suitable smoothing filter function in the frequency domain is the ratio of smooth and original (sharp) kernel. Efficient implementation can be achieved by a Fourier transform of this ratio to the spatial domain. Separating the two-dimensional spatial filtering into two subsequent one-dimensional filtering stages in the x and y directions using a Gaussian approximation for the convolution kernel further reduces computational complexity. Due to efficient implementation, interactive modification of the filter settings becomes possible, which can completely replace the variety of different reconstruction kernels.     

一种基于椒盐噪声新型组合滤波算法仿真研究

提出了一种新型组合滤波算法。该算法首先在噪声方差估计、滤波模板类型和尺寸大小等方面对自适应维纳滤波进行改进,对图像噪声进行预处理;其次将预处理后的图像进行二维多尺度小波分解,由于低频子图像基本不受噪声污染,故不作处理;然后对开关中值滤波分别从噪声检测、噪声分类、噪声滤波等方面进行改进,并给出具体实现步骤,用于小波域高频子图像滤波;最后将滤波后高频子图像和低频子图像进行小波系数重构。实验结果表明,两类改进滤波算法在滤波性能上均优于原始算法,在抗噪性和细节保持等方面具有一定优势。     

Reconstruction from projections under time-frequency constraints

Low-pass filtering computed tomography (CT) images to reduce noise may smooth or modify image features which are very important to the physician. Image features are often more easily identified and processed in the time-frequency plane. The authors use time-frequency distributions for spatially varying filtering of noisy CT images, constraining time-frequency representation coefficients of the projection data or of the reconstructed image to be zero in certain regions of the time-frequency plane. The authors consider two different applications: 1) filtering the projection data and then performing image reconstruction; and 2) filtering the reconstructed image directly. Criteria minimized, subject to constraints, may be either a deterministic minimum weighted perturbation of the given projection data or a stochastic minimum mean-square error in colored Gaussian noise. Results show improvement over processing the image with a linear spatially invariant filter.     

基于改进脊波变换的红外视频图像高质量复原

为了实现红外视频降质图像高质量复原,采用一种基于改进脊波变换的图像复原算法来进行处理。该算法首先对降质图像进行改进脊波变换,然后提出一种具有加权改进自适应伪中值滤波算法对脊波系数进行处理,最后针对滤波后图像中时常出现的"环绕"现象,引入自适应Wiener滤波算法来抑制。进行了理论分析和实验验证,取得了相关模拟降质图像、真实降质图像以及峰值信噪比的测试数据。结果表明,该算法性能优于伪中值滤波以及两类脊波变换去噪算法。这对于实现红外视频降质图像复原研究是有帮助的。     

Improved Speech Denoising Algorithm Based on Discrete Fractional Fourier Transform

The speech signal and noise signal are the typical non-stationary signals,however the speech signa is short-stationary synchronously.Presently,the denoising methods are always executed in frequency domain due to the short-time stationarity of the speech signal.In this article,an improved speech denoising algorithm based on discrete fractional Fourier transform（DFRFT）is pre sented.This algorithm contains linear optimal filtering and median filtering.The simulation shows that it can easily eliminate the noise compared to Wiener filtering improve the signal to noise ratio（SNR）,and enhance the original speech signal.     

基于同态滤波与直方图均衡化的射线图像增强

针对同态滤波与直方图均衡化单独进行X射线图像增强时存在的不足,提出了在频域内将同态滤波与直方图均衡化结合使用的思想.首先,对X射线图像进行同态滤波的分频处理;再将得到的低频分量进行全局的直方图均衡化处理;最后,将高频分量跟低频分量进行线性融合.实验结果表明,经过该方法处理的X射线图像,边缘信息更加突出,且整体视觉效果更明亮清晰.通过分析均方根误差和信噪比数据,也证实了该方法能有效地增强X射线图像.     

中值滤波在图像处理中的应用

通常在实际获取的图像中存在一定的噪声,而噪声的存在势必影响图像的清晰度、对比度等因素,会直接影响到下一步的图像处理.因此,图像滤波技术的研究一直是国内外研究的热点.中值滤波是一种典型的非线性滤波技术,能有效滤除干扰噪声.文中探讨了中值滤波算法原理及其在图像处理中的应用,并给出了采用MATLAB仿真的实例,结果表明其具有较好的实用价值.     

紫外图像处理方法

针对紫外成像存在噪声干扰的问题,为获取高品质的紫外图像及对目标准确检测,对典型紫外成像系统进行了研究。分析了紫外成像噪声干扰原因,建立了紫外目标场景模型。并以此研究紫外图像复原方法,对单幅图像进行门限处理,去掉背景噪声。然后对连续多帧图像进行均值处理和时域递归滤波,阈值分割后得到较好的图像输出。从图像输出结果可以看出,多帧图像均值处理和时域递归滤波都能有效地去除紫外图像中的随机噪声,特别是时域递归滤波方法对于图像复原有更好的效果。     

Seismic data filtering using a Gabor representation

Gabor representations are signal expansions using sets of functions that are localized and concentrated in time and frequency. This characteristic makes them suitable candidates for filtering data where the desired signal or noise is nonstationary or time-dependent. It is shown that Gabor representations formulated with frame theory can be used for time-dependent noise removal. Furthermore, their ability to filter noise in the presence of a nonstationary signal enables them to outperform singular value decomposition eigenimage filtering techniques in the removal of incoherent noise in common midpoint data with a moderate fold     

一种适用于椒盐-高斯干扰信号的自适应滤波改进算法

图像通信由于成像设备自身特点和通信过程中的光-电转换机制,一般含有椒盐-高斯干扰信号,信号交叉影响会导致单一的滤波方法效果不佳甚至失去作用。为了同时有效抑制两种干扰信号,提出了一种适用于椒盐-高斯干扰信号的自适应滤波改进算法。该算法首先通过干扰信号噪声点辨识与滤波窗口自适应扩展,计算信号噪声辨识过程中各扩展窗口归一化系数和一次加权联合滤波中间输出,然后利用多层级窗口中间输出值进行二次加权优化滤波,减少干扰信号噪声点对联合滤波输出的影响,最后针对计算量大的问题,在中值滤波过程中提出均值分割方法,提高滤波算法实时性。实验结果表明,该方法能有效抑制椒盐-高斯干扰信号噪声,算法实时性较好,优于多种传统及其演进滤波算法。     

一种改进型图像降噪方法

针对图像获取中易受到噪声干扰的问题,介绍了图像去噪处理的几种常用算法（邻域平均法、中值滤波法、图像间的平均滤波）,对比阐述了各自的优缺点及适用范围。提出了一种基于平滑滤波的小波阈值图像去噪算法,该算法采用中值滤波和小波阈值相结合的方式对图像进行平滑处理。实验结果表明,该方法不仅有利于图像噪声的去除,而且边缘信息也得到了较好的保留,使图像具有更好的视觉效果,还原出图像的本来面目。     

Local noise weighted filtering for emphysema scoring of low-dose CT images

Computed tomography (CT) has become the new reference standard for quantification of emphysema. The most popular measure of emphysema derived from CT is the pixel index (PI), which expresses the fraction of the lung volume with abnormally low intensity values. As PI is calculated from a single, fixed threshold on intensity, this measure is strongly influenced by noise. This effect shows up clearly when comparing the PI score of a high-dose scan to the PI score of a low-dose (i.e., noisy) scan of the same subject. In this paper, the noise variance (NOVA) filter is presented: a general framework for (iterative) nonlinear filtering, which uses an estimate of the spatially dependent noise variance in an image. The NOVA filter iteratively estimates the local image noise and filters the image. For the specific purpose of emphysema quantification of low-dose CT images, a dedicated, noniterative NOVA filter is constructed by using prior knowledge of the data to obtain a good estimate of the spatially dependent noise in an image. The performance of the NOVA filter is assessed by comparing characteristics of pairs of high-dose and low-dose scans. The compared characteristics are the PI scores for different thresholds and the size distributions of emphysema bullae. After filtering, the PI scores of high-dose and low-dose images agree to within 2%-3% points. The reproducibility of the high-dose bullae size distribution is also strongly improved. NOVA filtering of a CT image of typically 400 x 512 x 512 voxels takes only a couple of minutes which makes it suitable for routine use in clinical practice.     

X光序列图像三维动态滤波技术

对于静态X光图像序列,帧间滤波技术是抑制噪声、提高信噪比最为有效的方法之一。然而对于活动图像序列,该技术会使运动目标模糊,从而使图像质量更差。本文提出了一种X光序列图像的三维动态滤波技术,把图像序列作为二维空间数据流,在时间上采用先进先出的方式在整个积分周期内进行多帧平均,实现了沿着运动轨迹进行帧间滤波,从而使多帧平均技术对于活动X光图像序列同样具有较好的处理效果。     

两种改进型中值滤波算法比较

重点介绍了综合型中值滤波算法以及一种自适应中值滤波算法．针对这两种改进型中值滤波算法,对含有高斯噪声,椒盐噪声,混合噪声以及高密度噪声图像进行去噪处理,比较综合型中值滤波算法（文中采用了十字型和交叉型）和自适应中值滤波算法对不同图像的去噪效果．