基于噪声点多级检测的自适应中值滤波算法

针对传统中值滤波算法在图像去噪过程中造成较多图像细节信息丢失的问题,提出了一种基于噪声点多级检测的自适应中值滤波算法。该算法根据像素的空间相关性,逐级检测不同空间特征的噪声点。首先根据滤波窗口中相近像素点的数量来检测空间孤立的单个噪声点;然后通过扩展邻近窗口的方式检测空间连续的两个噪声点;接着进一步增加约束条件对空间连续的三个及以上的噪声点进行检测;最后对判断为噪声的像素用滤波窗口的中值替换。此外,该算法还能通过自适应地调整像素空间相关性判别阈值来处理不同分布特征的噪声。实验结果表明,与现有中值滤波算法相比,算法在有效滤除图像噪声的同时能更好地保护图像细节信息。     

基于阈值判断的自适应中值滤波算法

针对标准的中值滤波算法在去除噪声与保留图像细节方面难以取舍的缺陷,在自适应中值滤波算法的基础上提出了一种改进的基于噪声点检测的自适应中值滤波算法.该算法在进行噪声点检测时采用了一种阈值判断法,充分利用了当前像素点与邻域像素点的灰度值之间的关系.结果表明,在噪声浓度较高时仍然可以区分噪声点与边缘点,滤波的同时有效地保护了图像的细节.     

实时中子照相系统图像斑状噪声迭代滤波算法

根据实时中子辐照图像“斑状”噪声的成因和特点,提出一种有效的迭代滤波算法。设计十字形噪声检测窗口,通过计算邻域一致性测度(NHM),将像素分为噪声和信号。对噪声进行迭代滤波,而对信号则不做任何处理。滤波是一个中值计算过程,同时窗宽可自适应调整。这种方案不仅避免了噪声在邻域的传播,且有较高的计算效率。实验结果表明,对于峰值信噪比(PSNR)为24.51dB的噪声污染图像,3×3中值滤波后PSNR只有26.81dB,而本文算法能将其提高到31.54dB,同时图像的视觉效果更好。     

基于梯度的自适应椒盐噪声滤波算法

针对传统中值滤波方法不能有效保持图像细节信息和对图像适应能力差的问题,提出一种改进的椒盐噪声滤除算法。算法基于先检测、后滤波的思想,根据图像的极小梯度矩阵自适应计算噪声阈值,提高了噪声检测的准确性;为了更好地保持图像细节,对检测出的噪声像素进行多窗口中值滤波。多组去噪实验表明：所提算法对污染程度不同的图像具有良好的适应性,在滤除噪声的同时还可以有效还原图像边缘等细节信息。     

基于HSI颜色空间噪声检测的彩色图像去噪算法

目的为了有效去除彩色图像中的椒盐噪声,提高彩色图像质量。方法采用椒盐噪声检测和中值滤波相结合的方法,提出一种基于HSI颜色空间噪声检测的彩色图像去噪算法。将图像转换到HSI颜色空间,根据椒盐噪声在S通道具有极大值或极小值的特点判断出可疑椒盐噪声的位置,在H通道、I通道将可疑椒盐噪声分为噪声点和有用信号,对检测出的噪声像素进行中值滤波去噪。结果采用文中算法去噪后,验证图像主观评价值(Z)为1.30,平均PSNR为37.54,SSIM为0.99,Entropy为7.31,在主客观评价上优于现在常用算法。结论文中提出算法可以为彩色图像椒盐噪声的去噪提供理论基础,具有一定的实际应用价值。     

一种椒盐噪声点的检测算法技术研究

针对中值滤波算法虽然能够在很大程度上改善噪声带来的影响,但是使图像边缘变得模糊这一问题,提出了一种检测椒盐噪声的新算法。使用该算法对图像进行噪声检测后再对噪声点进行中值滤波能够有效地去除椒盐噪声。实验证明该算法具有明显的去噪能力,且当椒盐噪声比较严重时,也能够取得较好的滤波效果。     

应用多通道脉冲噪声检测的彩色图像自适应中值滤波方法

首先对彩色图像RGB 3个颜色通道的子图像,以串行的方式,确定出各颜色通道的脉冲噪声位置。根据待检测像素点附近4个邻域的灰度均值,自适应调整脉冲噪声判断阈值。由各通道的噪声检测结果经运算得到彩色图像脉冲噪声检测结果。采用改进的自适应矢量中值滤波法,对脉冲噪声有选择地滤除。实验结果表明,该方法结合标量方法进行脉冲噪声检测,矢量方法滤除彩色图像中的脉冲噪声,具有较好的滤波性能指标和视觉效果。     

噪声概率快速估计的自适应椒盐噪声消除算法

提出一种可识别噪声概率自动调节滤波窗口的自适应椒盐噪声消除算法。对非理想椒盐噪声污染图像随机区域进行变窗口中值滤波,将结果与滤波前比对获得噪声点数,滤波区域即按此点数排序。然后取每种滤波窗口下的中间三组数据,该数据平均加权获取图像噪声概率初估计,对初估计平均加权即得图像噪声概率。滤波前首先采用阈值法排除明显噪声点,剩余像素中再以离窗口中心像素距离平方的倒数为权值估计中心像素。最后由噪声概率按照T-S模糊规则对不同模型的输出估计值进行融合。实验证明,与传统中值滤波等算法相比,该算法具有噪声自动估计和自适应窗口调节能力,滤波后标准均方差可减少20%以上,速度可提高一倍多。     

基于GA-BP算法噪声检测的椒盐噪声滤波算法

基于噪声检测的中值滤波器已广泛用于消除图像中的椒盐噪声,然而在高噪声密度情况下,对噪声像素的定位不准确很容易造成图像边缘的模糊.本文提出了一种基于GA-BP的椒盐噪声滤波算法,克服了这一缺陷.算法首先用遗传算法优化的BP网络对图像中的噪声像素定位,然后引入保边函数和PRP算法求目标函数的极值进而实现图像的去噪处理.实验...     

泡罩药品包装视觉检测系统图像滤波算法

目的有效滤除泡罩药品包装视觉检测系统中的图像噪声,以保证后续泡罩包装药品的图像分割、特征提取等任务顺利开展。方法分析引导滤波的数学模型,并在此基础上对其进行改进,提出一种自适应高斯引导的新型泡罩药品包装图像滤波算法。采用更加理性的参数优化算法,且用高斯窗口来代替传统的盒窗口。结果自适应高斯引导滤波算法能够有效滤除图像中的噪声,同时最大程度地保留原始图像中的边缘和细节等信息。结论该算法对后期泡罩药品包装的图像处理和分析有一定的指导意义。     

Classifier-augmented median filters for image restoration

Developed in this paper is a new approach that augments a fuzzy classifier to determine whether or not the operating pixel, centered in the sliding window, should be involved with the impulse noise filtering process. Owing to the inclusion of the fuzzy K-nearest neighbor (K-NN) scheme, any central operating pixel that is not noise corrupted can be effectively detected and then left unchanged. Thus, the unnecessary pixel replacement can be avoided and the details and signal structure of the image will be best retained. If the center point is found to be noise corrupted, the proposed classifier-augmented median filter facilitates the filtering action only on a subset of pixels, which are not noise contaminated in the window. Due to this impulse pixel exclusion, the biased estimation caused from impulses can be eliminated and, thus, obtains a better estimation of the center pixel. Experimental results showed that this new approach largely outperformed several existing schemes for image noise removal.     

Filter for biomedical imaging and image processing

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a priori, knowledge about the type of noise corrupting the image. This makes the standard filters application specific. Widely used filters such as average, Gaussian, and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high-frequency details, making the image nonsmooth. An integrated general approach to design a finite impulse response filter based on Hebbian learning is proposed for optimal image filtering. This algorithm exploits the interpixel correlation by updating the filter coefficients using Hebbian learning. The algorithm is made iterative for achieving efficient learning from the neighborhood pixels. This algorithm performs optimal smoothing of the noisy image by preserving high-frequency as well as low-frequency features. Evaluation results show that the proposed finite impulse response filter is robust under various noise distributions such as Gaussian noise, salt-and-pepper noise, and speckle noise. Furthermore, the proposed approach does not require any a priori knowledge about the type of noise. The number of unknown parameters is few, and most of these parameters are adaptively obtained from the processed image. The proposed filter is successfully applied for image reconstruction in a positron emission tomography imaging modality. The images reconstructed by the proposed algorithm are found to be superior in quality compared with those reconstructed by existing PET image reconstruction methodologies.     

A novel learning-based switching median filter for suppression of impulse noise in highly corrupted colour images

A novel switching median filter integrated with a learning-based noise detection method is proposed for suppression of impulse noise in highly corrupted colour images. Noise detection employs a new machine learning algorithm, called margin setting (MS), to detect noise pixels. MS detection is achieved by classifying noise and clean pixels with a decision surface. MS detection yields very high detection accuracy, i.e. a zero miss detection rate and a fairly low over detection rate for a wide range of noise levels. After noise detection, a new filter scheme called the noise-free two-stage (NFTS) filter is triggered. NFTS corrects the noise pixels using the median of the noise-free pixels in two stages. The results of experiments have demonstrated that the MS based NFTS (MSN) filter is superior to the support vector machine and neural network for denoising highly corrupted images, in terms of noise suppression and detail preservation.     

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。     

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。     

Poisson noise reduction from X-ray images by region classification and response median filtering

Medical imaging is perturbed with inherent noise such as speckle noise in ultrasound, Poisson noise in X-ray and Rician noise in MRI imaging. This paper focuses on X-ray image denoising problem. X-ray image quality could be improved by increasing dose value; however, this may result in cell death or similar kinds of issues. Therefore, image processing techniques are developed to minimise noise instead of increasing dose value for patient safety. In this paper, usage of modified Harris corner point detector to predict noisy pixels and responsive median filtering in spatial domain is proposed. Experimentation proved that the proposed work performs better than simple median filter and moving average (MA) filter. The results are very close to non-local means Poisson noise filter which is one of the current state-of-the-art methods. Benefits of the proposed work are simple noise prediction mechanism, good visual quality and less execution time.     

基于自适应均值的图像滤波算法

目的为了提高激光三维成像系统中的图像质量,有效滤除图像中噪声,提出一种自适应均值漂移的图像滤波算法。方法在传统算法基础上对均值漂移滤波算法进行改进,选取领域内像素的均方差为控制参量对带宽矩阵h大小进行自适应调控。根据宽带矩阵h的大小,选择合适的像元值参与到计算均值过程中,以提高结果的计算精度。结果实验结果表明改进后的算法能够有效滤除图像中的噪声,提高图像清晰度。结论该算法具有良好的保边去噪特性。