基于噪声检测的自适应中值滤波算法

针对现有中值滤波算法对于高密度噪声图像以及纹理细腻图像的边缘处理能力欠佳的缺陷,提出一种基于噪声检测的自适应中值滤波算法.新算法根据噪声点与周围信息的关联程度将噪声点滤波值进行调整,从而更好的处理图像的细节部份.新算法中的自适应策略加强了滤波算法的去噪性能,使其对于含有任意噪声密度的图像也能很好的进行噪声滤除.通过仿真分析,新算法对于细节丰富的图像以及高密度噪声的图像滤波效果良好,有效的提高图像的峰值信噪比,其去噪效果相比其他方法更加优秀.     

基于混合滤波算法的数字图像去噪方法研究

双边滤波已广泛应用于数字图像处理领域,但在图像的高梯度区域,双边滤波会产生阶梯效应。双边滤波是局部模式滤波的一种特殊形式,提出了基于高斯滤波和双边滤波的混合图像去噪方法。利用高斯滤波器对噪声图像进行滤波,得到参考图像,将参考图像和噪声图像作为范围核函数的输入双边滤波器。参考图像提供图像的低频信息,噪声图像提供图像的高频信息。与传统双边滤波法比较,混合去噪方法能够有效地克服阶梯效应,滤波后的图像更平滑,纹理特征更接近原始图像,可获得更高的峰值信噪比。     

基于结构图像先验与ASM能量的服装图像去噪

服装图像在采集和传输过程中会受到噪声不同程度的影响,为更有效地去除服装图像中的噪声,本文提出了一种基于ASM图像能量的深度学习图像去噪方法.该方法基于结构图像先验理论,以随机向量作为卷积神经网络的输入,含噪声的服装图像作为目标输出.网络通过反向传播进行迭代,根据噪声与自然图像对于网络的阻抗不同,迭代至输出图像的ASM能量极大值处进行截断,截断处的输出图像即为去噪后的服装图像.实验结果表明,该方法对服装图像去噪后的PSNR达到29.91,比NLM去噪提高了 0.74,比guided去噪提高了 1.97.与传统的图像滤波去噪算法相比,该方法能更有效地去除图像中的噪声,保留服装图像的纹理细节.     

基于中值滤波去噪算法的激光光谱图像数据库构建

在目前建立的激光光谱图像数据库中,数据库的滤波性能较差,导致存储的光谱图像数据受噪声影响严重.因此,提出基于中值滤波去噪算法的激光光谱图像数据库构建.首先利用多数据源数据库模式,建立数据库的存储引擎.其次利用中值滤波去噪算法,在数据库中添加去噪模块.然后将滤波后的图像进行光学参数转换,最后对不同光谱数据分别进行格式转换...     

一种基于卡通纹理分解的X光图像恢复方法*

针对X光图像去噪时在抑制噪声的同时会模糊图像边缘的情况,提出采用图像卡通纹理分解和基于全变分的增广拉格朗日算法进行图像恢复.图像可以分解为卡通部分和纹理部分,噪声信息及图像的快变信息被分离到图像的纹理部分.通过基于增广拉格朗日算法的全变分去噪模型对纹理图像进行去噪处理,将卡通图像与处理后的纹理图像加权合成得到恢复图像.仿真实验结果表明,该方法不仅可以对图像进行快速处理,而且能够较好地保持图像的边缘信息,获得较高的输出信噪比.     

基于图像中值灰度相似度函数加权曲率滤波算法

曲率滤波算法通过构造滤波算子快速优化变分模型,但全变分曲率滤波及高斯曲率滤波易致去噪过平滑且椒盐噪声去除较差.提出了基于图像中值灰度相似度函数加权曲率滤波算法,其中,中值灰度相似度函数方差取决于小波变换最高频子带系数,能较好防止图像过平滑,且提高椒盐噪声去除能力;因此,采用中值灰度相似度函数分别对局部高斯曲率与局部全变分曲率投影算子加权,并分别迭代局部加权高斯曲率投影算子与局部加权全变分曲率投影算子,直至输出图像梯度总能量满足停止条件.实验表明,基于图像中值灰度相似度函数加权全变分曲率滤波与加权高斯曲率滤波比传统全变分曲率滤波和高斯曲率滤波去噪效果更好.     

一种改进的自适应分数阶积分图像去噪算法

为了改善图像去噪的效果,提出一种基于分数阶积分和中值滤波的改进自适应图像去噪算法,首先利用自适应中值滤波算法(Ranked-order Based Adaptive Median Filter,RAMF)中的噪声判别条件来检测噪声点,然后利用"噪声边缘"判别函数对其中的可疑噪声点进行二次检测,同时根据图像的局部统计信息和结构特征构造自适应的分数阶阶次,最后将检测出的噪声点进行自适应的分数阶积分滤波去噪。与传统的分数阶积分去噪算法相比,该自适应算法有效地保留了被错误误去除的图像边缘点,并且实现了分数阶积分的阶次自适应化,在去除噪声的同时很好地保留了图像的边缘及纹理细节信息。     

基于LWT和递归最小类内绝对差的红外小目标检测

针对存在背景干扰和噪声情况下的红外弱小目标检测问题,提出一种基于提升小波变换(LWT)和递归最小类内绝对差的检测方法.一方面先利用提升小波对原始图像进行去噪,再利用Top-hat算子抑制背景;另一方面先利用Top-hat算子抑制原始图像的背景,经提升小波去噪后,再进一步使用Top-hat算子;上述两方面得到的图像求和即为预处理图像.然后采用递归最小类内绝对差阈值选取方法分割预处理图像.针对红外小目标图像进行了大量实验,并与基于形态滤波及基于小波和形态学的红外小目标检测方法进行了比较.结果表明本文方法提高了信噪比,检测率分别提高15%和10%.     

一种图像去噪的小波相位滤波改进算法

大多数的小波去噪方法都是基于图像小波幅度信息的,但对于低SNR图像来说,其小波域中的图像边缘信息被噪声掩盖,所以有人提出了对幅度不敏感的小波相位滤波算法,利用含噪图像分解后的相位信息来恢复图像,本文对这种算法作出了一些改进。在相位滤波的基础上,考虑Laplace邻域,试验结果表明比原算法效果好。     

非线性调频信号的自适应时频滤波算法

针对传统的时域或频域滤波算法对非线性调频信号滤波去噪效果不好的问题,本文提出了一种时频域内非线性调频信号的自适应滤波去噪算法。首先对原信号进行广义S变换获得其时频分布,接下来利用有效信号时频分布特性选取时频通域,构造区域滤波算子并去除掉时频通域外的噪声分量的时频分布;然后利用有效信号分量的时频聚集性构造自适应时频滤波算子,对含有随机噪声的有效信号分量进行滤波处理,得到滤波去噪后的信号的时频分布;最后利用广义S逆变换将处理后的时频分布变换到时间域,得到滤波去噪后的信号。通过仿真实验的结果可知,本文提出的算法在非线性调频信号的滤波去噪和有效特性保持方面取得了较好的效果。      

Image denoising by supervised adaptive fusion of decomposed images restored using wave atom,curvelet and wavelet transform

This paper presents an efficient image denoising method that adaptively combines the features of wavelets, wave atoms and curvelets. Wavelet shrinkage is used to denoise the smooth regions in the image while wave atoms are employed to denoise the textures, and the edges will take advantage of curvelet denoising. The received noisy image is firstly decomposed into a homogenous (smooth/cartoon) part and a textural part. The cartoon part of the noisy image is denoised using wavelet transform, and the texture part of the noisy image is denoised using wave atoms. The two denoised images are then fused adaptively. For adaptive fusion, different weights are chosen from the variance map of the denoised texture image. Further improvement in denoising results is achieved by denoising the edges through curvelet transform. The information about edge location is gathered from the variance map of denoised cartoon image. The denoised image results in perfect presentation of the smooth regions and efficient preservation of textures and edges in the image.     

Multifeature extracting CNN with concatenation for image denoising

Convolutional neural networks (CNNs) have made great achievements in the field of image denoising but can still be improved. We introduce a network structure, namely, multifeature extracting CNN with concatenation (McCNN), which can preserve the edge and detail information and make the denoised image easier to view. The McCNN uses different-sized convolutional kernels to extract multiple features from the input image and send them into a forward network structure after cascading these features. The forward network structure consists of five nonlinear mapping modules, which are responsible for extracting more detailed textures and other advanced features. A skip connection is integrated into the forward network structure to pass the feature maps that carry many image details, which helps to reduce image distortion. The skip connection can also reduce gradient disappearance and improve network convergence speed. The potential clean image in the contaminated image contains much more information than the noise image. The noise image is regarded as the learning objective of the network to reduce the learning burden. The experimental results demonstrate that our McCNN denoising method can effectively remove Gaussian noise in grayscale images and offers objective and subjective quality improvement compared to that of the DnCNN-S, SCNN, and DSNet models, as well as other state-of-the-art denoising methods.     

一种新的小波图像去噪方法

小波图像去噪已经成为目前图像去噪的主要方法之一，目前的研究主要集中于如何选取阈值使去噪达到较好的效果。边缘信息是图像最为有用的高频信息，在图像去噪的同时，应尽量保留图像的边缘信息，基于这一思想，提出一种新的小波图像去噪方法。用数学形态学算子对图像小波变换后的小波系数进行处理，以去除具有较小支持域的噪声，保留具有连续支持域的边缘。实验结果表明，与普通的小波阈值去噪方法相比，该方法不但可以保留图像的边缘信息，而且能提高去噪后图像的峰值信噪比2～5dB，提高信噪比6～10dB。     

A generic denoising framework via guided principal component analysis

Though existing state-of-the-art denoising algorithms, such as BM3D, LPG-PCA and DDF, obtain remarkable results, these methods are not good at preserving details at high noise levels, sometimes even introducing non-existent artifacts. To improve the performance of these denoising methods at high noise levels, a generic denoising framework is proposed in this paper, which is based on guided principle component analysis (GPCA). The propose framework can be split into two stages. First, we use statistic test to generate an initial denoised image through back projection, where the statistical test can detect the significantly relevant information between the denoised image and the corresponding residual image. Second, similar image patches are collected to form different patch groups, and local basis are learned from each patch group by principle component analysis. Experimental results on natural images, contaminated with Gaussian and non-Gaussian noise, verify the effectiveness of the proposed framework.     

Image-pair-based deblurring with spatially varying norms and noisy image updating

This paper presents a deblurring method that effectively restores fine textures and details, such as a tree’s leaves or regular patterns, and suppresses noises in flat regions using consecutively captured blurry and noisy images. To accomplish this, we used a method that combines noisy image updating with one iteration and fast deconvolution with spatially varying norms in a modified alternating minimization scheme. The captured noisy image is first denoised with a nonlocal means (NL-means) denoising method, and then fused with a deconvolved version of the captured blurred image on the frequency domain, to provide an initially restored image with less noise. Through a feedback loop, the captured noisy image is directly substituted with the initially restored image for one more NL-means denoising, which results in an upgraded noisy image with clearer outlines and less noise. Next, an alpha map that stores spatially varying norm values, which indicate local gradient priors in a maximum-a-posterior (MAP) estimation, is created based on texture likelihoods found by applying a texture detector to the initially restored image. The alpha map is used in a modified alternating minimization scheme with the pair of upgraded noisy images and a corresponding point spread function (PSF) to improve texture representation and suppress noises and ringing artifacts. Our results show that the proposed method effectively restores details and textures and alleviates noises in flat regions.     

基于稀疏表示的红外图像去噪算法研究

红外图像具有动态范围窄、对比度低、易受噪声污染等缺点,传统红外图像去噪算法在去除噪声的同时也滤掉了图像细节。提出了一种基于稀疏表示的红外图像去噪新方法。该方法首先将原始红外图像进行聚类分析,再将每一聚类子图像分解成字典,由稀疏系数矩阵重构去噪后的红外图像。实验结果表明,该方法相比于传统红外图像去噪算法,能更好地保留图像的细节信息,视觉效果比较理想。     

An edge fusion scheme for image denoising based on anisotropic diffusion models

In this paper, we propose an enhanced anisotropic diffusion model. The improved model can classify finely image information as smooth regions, edges, corners and isolated noises by characteristic parameters and gradient variance parameter. And for different image information the eigenvalues of diffusion tensor are designed to conduct adaptive diffusion. Moreover, an edge fusion scheme is posed to preserve edges after denoising by combing different denoising and edge detection methods. Firstly, different denoising methods are applied for noisy image to obtain denoised images, and the best method among them is selected as main method. Then edge images of denoised images are obtained by edge detection methods. Finally, by fusing edge images together more integrated edges can be achieved to replace edges of denoised image obtained by main method. The experimental results show the proposed model can denoise meanwhile preserve edges and corners, and the edge fusion scheme is accurate and effective.     

Variational denoising of partly textured images by spatially varying constraints.

Denoising algorithms based on gradient dependent regularizers, such as nonlinear diffusion processes and total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. We propose a mechanism that better preserves fine scale features in such denoising processes. A basic pyramidal structure-texture decomposition of images is presented and analyzed. A first level of this pyramid is used to isolate the noise and the relevant texture components in order to compute spatially varying constraints based on local variance measures. A variational formulation with a spatially varying fidelity term controls the extent of denoising over image regions. Our results show visual improvement as well as an increase in the signal-to-noise ratio over scalar fidelity term processes. This type of processing can be used for a variety of tasks in partial differential equation-based image processing and computer vision, and is stable and meaningful from a mathematical viewpoint.     

分数阶微积分对频域除噪技术的改进

频域除噪是信号降噪技术中的重要内容。由于噪声多为高频,频域除噪技术一般利用低通滤波器滤除含噪信号中的高频部分,以达到除噪的目的。然而图像信号的轮廓以及某些细节部分也为高频,会被低通滤波器当作噪声滤除,降低除噪效果。利用分数阶微积分,对滤波器算法进行细微变换和调整,使其能够更加精确地区分噪声与高频信号,从而在滤除噪声的同时更多地保留高频信号部分。文中通过大量的仿真,证明了该方法较传统的滤波除噪技术具有很大的进步性。     

Gamma norm minimization based image denoising algorithm

Focusing on the issue of rather poor denoising performance of the traditional kernel norm minimization based method caused by the biased approximation of kernel norm to rank function,based on the low-rank theory,a gamma norm minimization based image denoising algorithm was developed.The noisy image was firstly divided into some overlapping patches via the proposed algorithm,and then several non-local image patches most similar to the current image patch were sought adaptively based on the structural similarity index to form the similar image patch matrix.Subsequently,the non-convex gamma norm could be exploited to obtain unbiased approximation of the matrix rank function such that the low-rank denoising model could be constructed.Finally,the obtained low-rank denoising optimization issue could be tackled on the basis of singular value decomposition,and therefore the denoised image patches could be re-constructed as a denoised image.Simulation results demonstrate that,compared to the existing state-of-the-art PID,NLM,BM3D,NNM,WNNM,DnCNN and FFDNet algorithms,the developed method can eliminate Gaussian noise more considerably and retrieve the original image details rather precisely.