一种自适应的图像双边滤波方法

提出一种利用双边滤波的图像平滑滤波方法,即在滤除图像中高频噪声的同时,按照图像亮度变化保持图像中处于高频部分的边缘信息的自适应滤波过程。该滤波方法将传统的Gauss滤波器的权系数优化成Gauss函数和图像的亮度信息乘积的形式,优化后的权系数再与图像作卷积运算。这样,滤波时就可以考虑到图像的亮度信息,在滤除图像噪声的同时尽量保持了图像的边缘。由于双边滤波的方法可以使滤波器的权系数随着图像的亮度变化而改变,所以在滤波过程中能达到自适应滤波的目的。     

去除脉冲噪声的自适应开关中值滤波

为消除图像中的脉冲噪声,提出了自适应开关中值(ASM)滤波算法。该算法采用一种新的噪声检测方法将图像中的像素分为信号点和噪声点两类。对检测出的噪声点统计其个数并由此估算图像中的噪声密度,根据估计的噪声密度自适应确定滤波窗口尺寸,采用改进的中值滤波对检测出的噪声点进行处理;而信号点则保留其灰度值不予处理。对ASM滤波进行仿真实验,结果表明,它能在有效去除噪声的同时很好地保护图像细节,较传统中值滤波及其它改进中值滤波算法有更优的滤波性能。     

Neural network-based image reconstruction for positron emission tomography

Positron emission tomography (PET) is one of the key molecular imaging modalities in medicine and biology. Penalized iterative image reconstruction algorithms frequently used in PET are based on maximum-likelihood (ML) and maximum a posterior (MAP) estimation techniques. The ML algorithm produces noisy artifacts whereas the MAP algorithm eliminates noisy artifacts by utilizing availableprior information in the reconstruction process. The MAP-based algorithms fail to determine the density class in the reconstructed image and hence penalize the pixels irrespective of the density class and irrespective of the strength of interaction between the nearest neighbors. A Hebbian neural learning scheme is proposed to model the nature of interpixel interaction to reconstruct artifact-free edge preserving reconstruction. A key motivation of the proposed approach is to avoid oversmoothing across edges that is often the case with MAP algorithms. It is assumed that local correlation plays a significant role in PET image reconstruction, and proper modeling of correlation weight (which defines the strength of interpixel interaction) is essential to generate artifact-free reconstruction. The Hebbian learning-based approach modifies the interaction weight by adding a small correction that is proportional to the product of the input signal (neighborhood pixels) and output signal. Quantitative analysis shows that the Hebbian learning-based adaptive weight adjustment approach is capable of producing better reconstructed images compared with those reconstructed by conventional ML and MAP-based algorithms in PET image reconstruction.     

闪光CCD图像的中值-非线性扩散滤波

根据闪光CCD图像的特点,提出了一种中值-非线性扩散滤波(Median-NonlinearDiffusionFiltering,简称MNDF)方法。该方法采用中值预滤波来估计图像的真实边缘,通过求解偏微分方程(PartialDifferentialEquation,简称PDE)来进行非线性扩散滤波,充分发挥了中值滤波和非线性扩散滤波的优势,能更好地消除噪声、保护边缘。实验结果表明,在高斯噪声和脉冲噪声同时存在的情况下,MNDF方法取得的滤波效果较P-M方案和Catte方案要好,信噪比改善因子提高3~5倍,均方误差减小1.3~2.7倍。对闪光照相CCD图像取得了很好的消噪声结果,保护了边缘信息。     

A fuzzy-based impulse noise detection and cancellation for real-time processing in video receivers

In this paper, a simple fuzzy-based algorithm to remove the impulse noise from images is proposed. To achieve real-time applications, the proposed filter architecture, which combines fuzzy noise detection and noise filtering, is also designed. With low computational complexity, simulation results show that the proposed filters effectively remove the impulse noise.     

基于像素同龄组的航拍图像去噪方法

由于受到光照、机身震动等拍摄环境的影响,航拍图像中常常混有高斯噪声和脉冲噪声。针对这一现象,提出一种结合改进的中值滤波和维纳滤波的像素同龄组去噪算法。首先将图像根据像素值的不同,分为若干像素同龄组;然后根据每个同龄组的特点,有针对性地进行中值滤波或维纳滤波;最后,借助航拍绝缘子图像,完成仿真实验,并与单独中值滤波、维纳滤波的去噪效果进行对比。实验结果表明,该方法在处理混有高斯噪声和脉冲噪声的航拍图像方面,具有良好的去噪效果。     

Impulse noise reduction using hybrid neuro-fuzzy filter with improved firefly algorithm from X-ray bio-images

Noise filtering performance in medical images is improved using a neuro-fuzy network developed with the combination of a post processor and two neuro-fuzzy (NF) filters. By the fact, the Sugeno-type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro-fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.     

Image denoising algorithms based on fractional sincα with the covariance of fractional Gaussian fields

Image denoising has been considered as an essential image processing problem that is difficult to address. In this study, two image denoising algorithms based on fractional calculus operators are proposed. The first algorithm uses the convolution of covariance of fractional Gaussian fields with the fractional sinc_α (FS) (sinc function of order α). The second algorithm uses the convolution of covariance of fractional Gaussian fields with the fractional differential Heaviside function, which is the limit of FS. In the proposed algorithms, the given noisy image is processed in a blockwise manner. Each processed pixel is convolved with the mask windows on four directions. The final filtered image based on either FS or fractional differential Heaviside function (FDHS) can be obtained by determining the average magnitude of the four convolution test results for each filter mask windows. The outcomes are evaluated using visual perception and peak signal to noise ratio. Experiments prove the effectiveness of the proposed algorithms in removing Gaussian and Speckle noise. The proposed FS and FDHS achieved average PSNR of 28.88, 28.26?dB, respectively, for Gaussian noise. The improvements outperform those achieved with the use of Gaussian and Wiener filters.     

基于凸包优化的盲源分离在图像去噪中的研究

目的针对高斯-脉冲混合噪声图像中难以有效去除大量奇异点或离群数据的问题,提出一种基于凸包优化的盲源分离方法来去除图像中的混合噪声。方法该方法把混合噪声和原图均看作未知的源信号,依据噪声图像中混合噪声与原图内容的加性关系建立盲源分离的模型,并利用凸包优化的方法构建源信号(凸包极点)的仿射包,然后通过最小化仿射包到凸包(噪声图像)上的投影误差,求解混合噪声和原图2个源信号,实现去噪混合噪声、复原原图的目的。结果实验结果发现,无论高斯-脉冲混合噪声强弱,该方法去噪复原后的峰值信噪比和平均结构相似性分别在39.9129 d B和0.9以上。结论由实验数据证实该方法可有效地从盲源分离的角度去除图像中高斯-脉冲混合噪声、复原原始图像。     

基于神经网络的图像混合滤波及融合算法研究

当图像中同时存在高斯噪声和椒盐噪声时,单一的均值滤波或中值滤波很难达到最佳滤波效果。 分析了噪声特点和各种滤波方法的优势,提出了一种基于神经网络的图像混合滤波及融合算法:首先建立概率神经网络,检测椒盐噪声和高斯噪声点,并分别利用中值滤波和均值滤波去除噪声点,然后建立径向基函数神经网络,利用训练好的径向基函数神经网络融合 2 种不同滤波的图像,输出理想的融合图像。 Matlab 仿真实验结果表明,该算法有效去除混合噪声的同时,能很好地保护图像的边缘与细节,是一种有效的方法。     

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。     

Image Denoising with Adaptive Weighted Graph Filtering

Graph filtering, which is founded on the theory of graph signal processing, is proved as a useful tool for image denoising. Most graph filtering methods focus on learning an ideal lowpass filter to remove noise, where clean images are restored from noisy ones by retaining the image components in low graph frequency bands. However, this lowpass filter has limited ability to separate the low-frequency noise from clean images such that it makes the denoising procedure less effective. To address this issue, we propose an adaptive weighted graph filtering (AWGF) method to replace the design of traditional ideal lowpass filter. In detail, we reassess the existing low-rank denoising method with adaptive regularizer learning (ARLLR) from the view of graph filtering. A shrinkage approach subsequently is presented on the graph frequency domain, where the components of noisy image are adaptively decreased in each band by calculating their component significances. As a result, it makes the proposed graph filtering more explainable and suitable for denoising. Meanwhile, we demonstrate a graph filter under the constraint of subspace representation is employed in the ARLLR method. Therefore, ARLLR can be treated as a special form of graph filtering. It not only enriches the theory of graph filtering, but also builds a bridge from the low-rank methods to the graph filtering methods. In the experiments, we perform the AWGF method with a graph filter generated by the classical graph Laplacian matrix. The results show our method can achieve a comparable denoising performance with several state-of-the-art denoising methods.     

一种强噪声背景下的微弱信号检测的新方法

基于自适应滤波算法对小波滤波器的构造方法进行了研究,重点在于解决对强噪声环境下微弱信号提取所需要的小波变换的子带自适应滤波器的构造,把小波变换技术和自适应滤波技术应用于微弱信号的检测。提出基于自适应小波变换的心电信号的检测,利用小波变换的子带编码理论,通过在多个子带权值的自适应匹配,回复后拟合微弱信号。仿真结果表明,该方法可进一步改善信号的检测能力,在检测微弱信号的特征和改善信噪比方面是一种十分有效的方法。     

基于各向异性扩散滤波与非局部先验的PET图像重建算法

针对在正电子发射断层成像中,经典的惩罚最小二乘算法和基于局部先验惩罚的改进算法不能有效地抑制噪声并保护图像边缘的问题,提出了一种新的各向异性扩散滤波非局部惩罚最小二乘重建算法(PLS-PDE).新算法首先对图像进行基于非局部二次先验的最小二乘估计,接下来对此估计值进行基于双向扩散系数的各向异性扩散滤波.所提出的算法综合了非局部先验与各向异性扩散滤波的优点.实验结果表明,新算法在抑制噪声和边缘保护方面取得了良好的折中,较大程度地改善了重建图像的质量.     

Elimination of zero-order diffraction and conjugate image in off-axis digital holography

The presence of zero-order diffraction and a conjugate image in digital holography essentially diminishes the quality of the reconstructed image. In this paper, a novel method that adopts numerical operation to eliminate the zero-order diffraction and conjugate image is presented. The whole process needs only one hologram and a complex finite impulse response (FIR) digital filter. The method of numerical elimination is simple; it filters the hologram directly in the spatial domain instead of in the frequency domain. The design of a complex finite impulse response filter is described in detail. The experimental results demonstrate that the operation can completely eliminate the zero-order diffraction and conjugate image and significantly enhance the quality of the reconstructed image.     

一种改进的基于对数谱估计的语音增强算法

提出了一种基于对数谱估计的改进型语音增强算法。相对于传统语音增强算法,在语音信号存在不确定的条件下,利用软判决增益因子修正技术调正带噪语音信号的对数谱幅度,抑制背景噪声。引入的改进型先验信噪比估计和语音信号先验不存在概率估计方法,能够有效地估计得出语音信号的存在概率,进而求得语音信号存在时的谱增益因子函数,联合语音信号不存在时设定的增益因子函数加权求得谱增益函数。计算机仿真表明,即使在低信噪比条件下,输入背景噪声为高斯白噪声和粉红噪声等加性白噪声时,所提算法对噪声的抑制效果非常明显,且有效地克服了传统算法中引入的“音乐噪声”和语音信号畸变。     

基于Retinex和同态滤波的X射线电池图像增强算法

针对工业X射线电池图像对比度低、视觉效果不好、不同区域处理效果差异大等问题,提出一种基于多尺度Retinex和同态滤波的X射线电池图像增强算法.首先利用多尺度Retinex算法中的高斯滤波对照射分量估计,从而得到反射分量.用改进的直方图均衡化方法来处理照射分量,采用改进的巴特沃斯高通滤波器对反射分量的局部细节增强.接着...     

A study of errors due to reconstruction filters in computed tomography

In the summation convolution backprojection method of image reconstruction in computed tomography, the final image accuracy depends on the convolution filter used. Filters are designed to attenuate high spatial frequencies when noisy projection data are used. This paper explores the differences between the images reconstructed using a range of filters, and compares the results with the case of the ramp filter that provides the “best” image for ideal, noise-free, projection data. It is shown that systematic errors between these images and the best image exist, and that these errors are related to the second differential of the reconstruction filter with respect to spatial frequency. This error determination may be used to correct computed tomography images that have been reconstructed using inappropriate filters, and this theory is tested using noise-free projection data from two computer simulated images. It is shown that the corrected images are far closer to the original images.     

一种结合时间轴滤波的视频消噪新方法

为了提高含噪视频图像的质量,提出了一种二维小波域自适应滤波与时域时间轴滤波相结合的视频图像消噪新方法。首先,对视频序列的各帧在二维小波域中进行自适应滤波,之后在时域中进行时间轴滤波。对于二维小波域滤波算法,提出了一种高效的自适应阈值选取方案;时间轴滤波器则是结合了运动检测和递归平均。实验结果表明,其消噪效果要优于单纯的二维小波域滤波方法。     

Sparse‐view statistical iterative head CT image reconstruction via joint regularization

It is a significant challenge to accurately reconstruct medical computed tomography (CT) images with important details and features. Reconstructed images always suffer from noise and artifact pollution because the acquired projection data may be insufficient or undersampled. In reality, some “isolated noise points” (similar to impulse noise) always exist in low‐dose CT projection measurements. Statistical iterative reconstruction (SIR) methods have shown greater potential to significantly reduce quantum noise but still maintain the image quality of reconstructions than the conventional filtered back‐projection (FBP) reconstruction algorithm. Although the typical total variation‐based SIR algorithms can obtain reconstructed images of relatively good quality, noticeable patchy artifacts are still unavoidable. To address such problems as impulse‐noise pollution and patchy‐artifact pollution, this work, for the first time, proposes a joint regularization constrained SIR algorithm for sparse‐view CT image reconstruction, named “SIR‐JR” for simplicity. The new joint regularization consists of two components: total generalized variation, which could process images with many directional features and yield high‐order smoothness, and the neighborhood median prior, which is a powerful filtering tool for impulse noise. Subsequently, a new alternating iterative algorithm is utilized to solve the objective function. Experiments on different head phantoms show that the obtained reconstruction images are of superior quality and that the presented method is feasible and effective.