On the performance of stack filters and vector detection in image restoration

Two techniques for image restoration are compared in this paper. One is a technique based on the theory of optimal adaptive stack filtering; the other is a recently developed vector detection approach to image restoration. The primary difference between these two techniques is that the optimal detection technique exploits multilevela priori information, while the stack filter uses only single level zero crossing information.The design constraints for stack filters and vector detection are similar. Both approaches rely on the existence of a training sequence for the image source in order to obtain optimal processing. Adaptive stack filters do, however, require a training set of the noise while the optimal detection approach only needs a multivariate parametric representation.The image-restoration performance of these two methods is compared in a signal dependent noise environment characterizing imaging systems with speckle, film-grain, and Poisson shot noise. Comparisons are made using the mean absolute error measure as well as a subjective measure.This research was sponsored in part by the A. I. DuPont Institute.     

基于EMCCD 的光子计数成像方法与实验

讨论了在复杂背景条件下采用EMCCD 实现光子计数成像的处理策略,提出了一种双重阈值滤波方法并通过实验验证了其有效性。根据EMCCD 的成像模型,将EMCCD 的输出图像表示为光子事件、偏置噪声和伪光子噪声事件的集合。通过采集多幅EMCCD 的输出图像,将光子事件检测问题转化为标准的二元假设检测问题。利用贝叶斯判决,可以得到光子事件检测的最佳检测法则,使光子事件的误判代价达到最小,从而检测出光子事件及其空间分布特征。通过实验对比可以发现:提出的方法明显优于普通的累加积分方法,适合生物微光检测等超低照度条件下的成像领域。     

Adaptive Neural Network for Nuclear Medicine Image Restoration

A novel adaptive neural network is proposed for image restoration using a nuclear medicine gamma camera based on the point spread function of measured system. The objective is to restore image degradation due to photon scattering and collimator photon penetration with the gamma camera and allow improved quantitative external measurements of radionuclides in-vivo. The specific clinical model proposed is the imaging of bremsstrahlung radiation using ³²P and ⁹⁰Y because of the enhanced image degradation effects of photon scattering, photon penetration and poor signal/noise ratio in measurements of this type with the gamma camera. This algorithm model avoids the common inverse problem associated with other image restoration filters such as the Wiener filter. The relative performance of the adaptive NN for image restoration is compared to a previously reported order statistic neural network hybrid (OSNNH) filter by these investigators, a traditional Weiner filter and a modified Hopfield neural network using simulated degraded images with different noise levels. Quantitative metrics such as the change of signal to noise ratio (SNR) are used to compare filter performance. The adaptive NN yields comparable results for image restoration with a slightly better performance for the images with higher noise level as often encountered in bremsstrahlung detection with the gamma camera. Experimental attenuation measurements were also performed in a water tank using two radionuclides, ³²P and ⁹⁰Y, typically used for antibody therapy. Similar values for an effective attenuation coefficient was observed for the restored images using the OSNNH filters and adaptive NN which demonstrate that the restoration filters preserves the total counts in the image as required for quantitative in-vivo measurements. The adaptive NN was computationally more efficient by a factor 4–6 compared to the OSNNH filter. The filter architecture, in turn, is also optimum for parallel processing or VLSI implementation as required for planar and particularly for tomographic mode of detection using the gamma camera. The proposed adaptive NN method should also prove to be useful for quantitative imaging of single photon emitters for other nuclear medicine tomographic imaging applications using positron emitters and direct X-ray photon detection.     

Edge-Preserving Regularized Filter with Spatial Local Outlier Measure and Q-Estimate

This paper presents an efficient random-valued impulse noise removal algorithm. The filtering process contains two phases: a detection phase followed by a filtering phase. In the detection phase, the proposed method uses the novel image statistics, the spatial local outlier measure (SLOM) and the Q-estimate, to identify impulses in a corrupted image. When the noise pixels are identified, their values are restored by an edge-preserving regularized method in the filtering phase. Extensive experimental results show that our filter provides a significant improvement over many other existing techniques.     

一种小波域与空域相结合的图像滤波方法

利用小波阈值去噪方法和传统空间域Lee 滤波的特点, 提出了一种图像去噪的的组合滤波方案。首先在小波域对图像阈值去噪, 得到预去噪图像; 再在空间域上利用自适应Wiener 滤波器进一步提高恢复图像的精度。为了保证小波域和空间域两种算法之间的匹配, 对预去噪图像中残留噪声的分布进行了研究, 对其噪声方差估计做了改进, 提出了一种估计噪声方差的近似最优公式。仿真实验表明, 与单独的在小波域或空域去噪相比, 该方法的均方误差和信噪比指标均得到了改善。     

噪声密度不敏感的随机采样椒盐噪声滤波算法

中值滤波系列算法在处理被不同密度椒盐噪声污染的细节图像和平坦图像时,降噪性能不一致。本文借鉴开关中值滤波和压缩感知的思想,提出了随机采样滤波算法去除椒盐噪声。算法以噪声检测为基础,将被椒盐噪声污染的图像分为疑似噪声像素和信号像素,随机采样仅对信号像素采样。然后,利用正交匹配追踪算法重构出被污染前的图像,替代了中值滤波对噪声像素的估计。由于随机采样滤波基于压缩感知理论,对稀疏信号的重构具有最少测量次数的条件,因此随机采样点的数量具有一定的浮动空间,表现为对噪声密度不敏感。以被不同噪声密度污染图像的纹理、平坦局部区域进行验证,实验表明,当噪声密度在一定范围内变化时,算法可以实现对噪声密度不敏感。在高密度噪声污染的情况下,相较于中值滤波系列算法,随机采样滤波算法具有更好的细节保留能力和滤波能力。对标准测试图像进行了全局滤波,不同噪声密度具有一致的滤波效果,与自适应滤波算法相比,随机采样滤波算法在处理包含密集边缘特征的区域时更具备优势。      

A Universal Impulse Noise Filter with an Impulse Detector and Nonlocal Means

In this paper, we propose an efficient filter for universal impulse noise removal. Operation is carried out in two stages: impulse detection followed by filtering. For detection, a robust local image statistic, called the extremum compression rank-order absolute difference (ECROAD), is designed to detect impulse noise in an image. For filtering, a universal impulse noise filter is proposed by combining the ECROAD statistic with the nonlocal means (NLM). The inherited switching behavior will preserve image details by selecting possible “noise pixels” for processing. Meanwhile, the joint impulsive weight is able to avoid the effect of impulsive components in restoring candidates. Simulation results show that the proposed filter produces excellent results and outperforms most existing filters for different impulse noise models.     

Wavelet-based Rician noise removal for magnetic resonance imaging

It is well known that magnetic resonance magnitude image data obey a Rician distribution. Unlike additive Gaussian noise, Rician "noise" is signal-dependent, and separating signal from noise is a difficult task. Rician noise is especially problematic in low signal-to-noise ratio (SNR) regimes where it not only causes random fluctuations, but also introduces a signal-dependent bias to the data that reduces image contrast. This paper studies wavelet-domain filtering methods for Rician noise removal. We present a novel wavelet-domain filter that adapts to variations in both the signal and the noise.     

基于数学形态学与自适应的超声医学图像滤波方法的研究

超声医学成像作为主要的医学影像技术之一,因其对人体无伤害、实时、价格便宜和使用方便等优点已广泛应用于临床.然而,在成像过程中形成的特有的图像斑点,使得对比度弱的人体软组织中的正常组织和病变组织不易区分,给临床诊断和医学研究带来不便.针对医学超声图像的特点,在研究了几种常用滤波方法后,提出一种自适应中值滤波和形态滤波结合的新方法,并做了实验验证.实验方法是:首先对所选择的医学超声图像施加瑞利噪声,然后采用中值滤波、自适应中值滤波的方法对被污染的图像进行去噪处理,接下来先采用自适应中值滤波对图像进行预处理,抑制斑点噪声,保留必要细节,再采用数学形态学方法进行二次滤波和增强对比度,进一步改善图像质量.最后从去噪图像和评价指标上对三种滤波去噪方法进行了比较.实验证明,新方法优于其他方法.     

Fuzzy two-step filter for impulse noise reduction from color images.

A new framework for reducing impulse noise from digital color images is presented, in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique. We call this filter the fuzzy two-step color filter. The fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning. This phase determines three separate membership functions that are passed to the filtering step. These membership functions will be used as a representation of the fuzzy set impulse noise (one function for each color component). Our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture. Experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image.     

基于LED辅助照明的水下图像增强算法研究

水下成像环境十分复杂,在成像过程中受到多种因素的影响,如光照的非均匀性,水对光的后向散射,成像器件的光学噪声,导致图像对比度低,细节模糊.针对水下图像的特点,提出运用同态滤波和双正交小波阈值滤波法相结合的图像增强算法改善水下图像光照的非均匀性、滤除噪声、提高水下图像的对比度,并通过峰值信噪比、均方差对该算法进行了性能评价.     

运动条件下光子计数激光测距系统点云数据的处理

研究了运动条件下光子计数激光测距系统的探测机制,分析了该系统在数据处理中涉及到的回波光子信号的统计特性、探测概率等理论问题。针对运动条件下的测距背景,根据该系统的回波信号特点,基于Matlab产生了符合统计分布规律的包含目标回波和噪声回波的仿真信号。在对泊松滤波算法等传统数据处理算法进行研究的基础上,提出了一种改进的基于栅格滤波的算法,并利用该算法对仿真回波数据进行了处理,最终有效滤除了随机分布于整个探测空间的噪声信号,获得了目标的运动轨迹。与传统的数据处理算法相比,该算法对运动条件下的光子计数激光测距数据具有良好的处理效果。     

基于MCP的快速光子计数成像系统的设计方案

通常基于MCP的阳极探测系统获得图像的速度受到噪声和信号处理速度的限制,影响了图像的时间分辨率和空间分辨率。介绍了一种快速光子计数成像探测器,使用的阳极结构避免了需要高信噪比的信号测量电路,及模拟信号到数字信号的转换,缩短了电子信号处理的时间,能有效提高图像质量。阳极面板依据格雷码编码方式划分区域,因此从微通道板出来的电子云在位敏阳极上的质心位置,由成对电极上的电荷比较值来确定,省略了由模拟信号变为数字信号的过程。所以对比较器的要求较高,要有高的运算速度,前端电子学具有高速、低噪、线性的特性。在此对快速光子计数成像系统提出了整体设计方案,并得到初步的实验结果,证实方案可行。     

Adaptive noise removal from biomedical signals using warpedpolynomials

Presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cutoff frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the author also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The author presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base-line restoration of electroencephalograms (EEGs)     

Quantitative image quality analysis of a nonlinear spatio-temporalfilter

Digital temporal and spatial filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low X-ray exposure. In this study, we characterized a nonlinear edge preserving, spatio-temporal noise reduction filter, the bidirectional multistage (BMS) median filter of Arce (1991). To assess image quality, signal detection and discrimination experiments were performed on stationary targets using a four-alternative forced-choice paradigm. A measure of detectability, d', was obtained for filtered and unfiltered noisy image sequences at different signal amplitudes. Filtering gave statistically significant, average d' improvements of 20% (detection) and 31% (discrimination). A nonprewhitening detection model modified to include the human spatio-temporal visual system contrast-sensitivity underestimated enhancement, predicting an improvement of 6%. Pixel noise standard deviation, a commonly applied image quality measure, greatly overestimated effectiveness giving 67% improvement in d'. We conclude that human testing is required to evaluate the filter effectiveness and that human perception models must be improved to account for the spatio-temporal filtering of image sequences.     

A vector Wiener filter for dual-radionuclide imaging

The routine use of a single radionuclide for patient imaging in nuclear medicine can be complemented by studies employing two tracers to examine two different processes in a single organ, most frequently by simultaneous imaging of both radionuclides in two different energy windows. In addition, simultaneous transmission/emission imaging with dual-radionuclides has been described, with one radionuclide used for the transmission study and a second for the emission study. There is thus currently considerable interest in dual-radionuclide imaging. A major problem with all dual-radionuclide imaging is the "crosstalk" between the two radionuclides. Such crosstalk frequently occurs, because scattered radiation from the higher energy radionuclide is detected in the lower energy window, and because the lower energy radionuclide may have higher energy emissions which are detected in the higher energy window. The authors have previously described the use of Fourier-based restoration filtering in single photon emission computed tomography (SPECT) and positron emission tomography (PET) to improve quantitative accuracy by designing a Wiener or other Fourier filter to partially restore the loss of contrast due to scatter and finite spatial resolution effects. The authors describe here the derivation and initial validation of an extension of such filtering for dual-radionuclide imaging that simultaneously 1) improves contrast in each radionuclide's "direct" image, 2) reduces image noise, and 3) reduces the crosstalk contribution from the other radionuclide. This filter is based on a vector version of the Wiener filter, which is shown to be superior [in the minimum mean square error (MMSE) sense] to the sequential application of separate crosstalk and restoration filters.     

旋转均值滤波用于提高红外点目标图像信噪比

当距离红外传感器很远时,目标在红外图像中表现为点目标。充分利用点目标,背景杂波及噪声在空间域中的表现形式,并结合均值滤波滤除噪声的特性,提出了一种新的滤波方法——旋转均值滤波法。将此算法运用在信噪比不大于3的红外点目标图像中,实现了背景抑制,突出目标以及提高信噪比作用,并能够满足实时处理的要求。     

Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction

This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected “noise pixels” will then be subjected to the second stage of the filtering action, while “noise-free pixels” are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature.      

Optimal a posteriori time domain filter for average evokedpotentials

Evoked potentials measured with scalp electrodes are often described as a deterministic process corrupted by unrelated noise. The common procedure to determine the signal is to average N repetitive measurements. By obtaining additional information from the N measurements, signal detection can be improved. An algorithm that estimates the signal autocorrelation from N given measurements is proposed. The estimator is consistent and unbiased, and its variance tends to 0 as o(N). Two filters that are applied to the average response are introduced. Both depend on the estimation of the signal and the noise autocorrelations. One filter is based on the assumption that the average response is a stationary process. The second filter coefficients are obtained by minimizing the mean squared error (MSE) of an optimal filter of a nonstationary process applied on a single sweep. When a small number of sweeps are averaged, the stationary assumption is adequate, and the MSE of the stationary optimal filter is two to five times less than the MSE of the average response. When a large number of measurements are considered, the error in estimating the autocorrelations decreases. In this case, applying the optimal filter for a nonstationary process leads to a significant improvement in the signal estimation.     

Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector

A predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector (PASMF) is presented. The PASMF has a noise detector stage and a noise filtering stage. The noise detector implemented using feed forward neural network detects impulse noises in the corrupted image. The filter is a modified median filter, which removes detected impulse noise from the image. In contrast to the standard median filter, the PASMF computes the median value after predicting the appropriate values for neighboring corrupted pixels of the current central pixel in the filtering window. The results show that the PASMF gives better performance visually as well as in terms of different performance measures.