基于Surfacelet变换视频自适应去噪

为了有效地保留视频去噪后的纹理细节,利用Surfacelet变换特性和自适应阈值的方法,提出了一种新的视频去噪算法。Surfacelet变换是超小波分析领域的最新发展,具有良好的多维信号处理能力,真正实现了对多维信号的多方向多尺度分解。本文介绍了Surfacelet变换的结构,根据Surfacelet变换的视频信号的系数特性和噪声分布特征,自适应估计每个方向子带的系数阈值。实验表明,本文算法能更好保留视频细节信息,对于视频的噪声也具有更强的抑制能力。     

BM3D视频去噪算法实现与评估

一些传统的图像视频去噪算法,会在滤除噪声的同时引入人工噪声,存在很大的局限性。而基于块匹配和三维变换域滤波(BM3D)的图像视频去噪算法则采用了不同的去噪策略。对BM3D算法进行了深入的研究并实现了算法,对算法进行了深层次的评估。通过评估结果,证明了BM3D算法在视频去噪方面取得了非常好的效果。     

基于非局部自相似序列集的一类视频图像盲去噪算法

为提高具有帧间位移平移特性的视频图像的信噪比和去噪时效,本文提出了基于非局部自相似序列集的视频图像盲去噪算法.选取与待去噪视频图像前后相邻的若干图像帧,在每一图像帧中寻找具有典型特征的图像块群,并通过在前一帧图像中查找和该图像块群具有最小差异度的块群来确定帧间的精确位移;将待去噪视频图像划分成若干图像块,根据帧间位移快速构建每个图像块的自相似序列集;随后将每个自相似序列集中的二维图像块整合成三维矩阵后进行三维变换,并对变换系数进行自适应阈值处理;再将三维逆变换后的图像块融合生成去噪图像.实验结果表明,在噪声方差未知的情况下,本文算法所得去噪视频图像具有较好的信噪比和视觉效果,并且有较高的运行效率.     

基于双域滤波的三维块匹配视频去噪算法

将双域滤波和三维块匹配算法相结合,充分利用了视频序列的时空频域的相关性,进行视频去噪。将三维块匹配视频去噪算法中小波阈值去噪得到的图像替换原引导图像,抑制了双域滤波算法基本层不平滑的问题。参考残差矩阵的能量谱对短时傅里叶变换系数进行阈值收缩,减少细节层的噪声残留。实验结果表明,本算法有效地解决了双域滤波算法的基本层不平滑、细节层有噪声残留的问题,主观和客观对比也表明,本算法有着较为优秀的去噪效果。     

BM3D算法在海洋SAR图像去噪中的应用

相干斑噪声一直是困扰SAR图像处理的重要问题,噪声往往将有用信号完全掩盖。由于海洋图像邻域相似性高,将三维块匹配(BM3D)算法应用于海洋SAR图像相干斑噪声的滤除中,能够得到较好的去噪效果。该算法是基于变换域方法和非局部思想发展而来的,通过相似图像块匹配分组生成三维矩阵,然后在三维变换域去噪,最后逆变换还原图像。通过对BM3D算法的深入研究,发现该算法去噪性能与其参数密切相关。针对海洋SAR图像,通过大量实验,总结BM3D参数选择原则,优化了算法的去噪性能。     

基于S1/2范数的非局部视频去噪

为了去除视频中的高斯噪声及脉冲噪声,提出了 一种基于S1/2矩阵范数的非局部视频去噪算法。 首先,在视频数据中利用非局部块匹配的钻石搜索算法搜寻与参考图像块最相似的图像块组 ;然后,将搜 寻到的相似图像块组列向量化后组合成的矩阵进行基于S1/2范数的低秩和稀疏分解,分解后 的低秩成分视 为原视频场景信息,稀疏成分视为视频中存在的随机值脉冲噪声及异常值数据;最后,由低 秩矩阵恢复的 各图像块数据经过加权平均后作为参考图像块的去噪估计值,进而求得视频各帧图像的去噪 估计值。实 验结果表明,本文方法能够有效去除视频中的高斯噪声和脉冲噪声,相比同类 算法,去噪后的视频无论 在视觉质量上还是客观评价指标上都有明显的优势。     

基于映射函数收缩算法的图像去噪方法

文中讨论了图像的高斯加性噪声模型和图像的稀疏性表示,提出了利用映射函数来描述图像的去噪过程,通过求解映射函数和利用映射函数对加噪图像的小波变换子带系数进行变换,达到了降低图像噪声并使加噪图像逼近原始图像的目的.经过实验比较,验证了本文算法的可行性和鲁棒性.     

基于Contourlet变换域统计模型的路面图像去噪算法

提出了一种基于Contourlet变换域统计模型的图像去噪算法,用于复杂背景下的路面病害图像,可以获得较为清晰的裂纹病害信息。利用χ2统计假设检验方法得出Contourlet变换系数近似服从拉普拉斯分布,进而应用最大后验贝叶斯估计推导出对含噪图像Contourlet系数的萎缩公式。实验结果表明,该算法能较好地实现噪声抑制和信号保留间的平衡,与小波去噪算法比较,其去噪后图像的信噪比提高4 dB。     

滤除图像中混合噪声的LSE模型

图像中的高斯白噪声使LS模型中的低秩矩阵低秩性和稀疏矩阵稀疏性不能同时满足,造成去噪不充分或细节严重丢失。本文在LS模型的基础上引入高斯噪声约束项,提出一种新的用于去除图像中混合噪声的LSE模型,该模型首先对图像进行相似块匹配,然后对得到的相似块低秩逼近得到去噪图像。实验结果表明,与LS模型相比,LSE模型在保证去噪效果的同时,保留了图像的细节信息,具有更佳的视觉效果,去噪图像的信噪比提高了约0.1-2dB;与BM3D相比,在高斯噪声较小的情况下信噪比提高了约0.5-2.5dB。      

基于噪声估计的自适应块匹配和三维滤波降噪算法

针对经典的块匹配和三维滤波(BM3D)降噪算法中最 为核心的噪声水平(方差)参数在使用中需要 人工手动设置极大影响了降噪效果并限制了它的应用,提出了一种新的基于自然场景 统计(NSS)的噪声水平特征矢量和支持向量回归(SVR)技术的快速噪声水平估计算法并应用于 经典BM3D算法 中,使之转变为自适应降噪算法(Adaptive BM3D)。本文算法首先利用小波变换对图像进行 不 同尺度和不同方向的分解,提取各子带滤波系数并用通用高斯分布模型(GGD)建模,以模型 参数构成反映噪 声图像噪声水平的特征矢量;然后用SVR方法在大量噪声图像样本上进行训练获得图像噪声 水平预测模型。 实验表明:改进后的ABM3D算法实际图像降噪效果比BM3D算法获得进一步提升,并且仍然 保持了非常高的执行效率,相对于当前各主流算法具有明显的竞争力。     

MR image denoising method for brain surface 3D modeling

The goal of Optoelectronics Letters is to rapidly report original, new and important results in the fields of photonics and optoelectronics in English, to advance the international academic exchanges. Optoelectronics Letters pays a particularly attention to the cross topics between photonics and electronics.     

Removal of Correlated Noise by Modeling the Signal of Interest in the Wavelet Domain

Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new denoising method for the removal of correlated noise, by modeling the significance of the noise-free wavelet coefficients in a local window using a new significance measure that defines the “signal of interest” and that is applicable to correlated noise. We combine the intrascale model with a hidden Markov tree model to capture the interscale dependencies between the wavelet coefficients. We propose a denoising method based on the combined model and a less redundant wavelet transform. We present results that show that the new method performs as well as the state-of-the-art wavelet-based methods, while having a lower computational complexity.      

A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising

This paper presents a new wavelet-based image denoising method, which extends a "geometrical" Bayesian framework. The new method combines three criteria for distinguishing supposedly useful coefficients from noise: coefficient magnitudes, their evolution across scales and spatial clustering of large coefficients near image edges. These three criteria are combined in a Bayesian framework. The spatial clustering properties are expressed in a prior model. The statistical properties concerning coefficient magnitudes and their evolution across scales are expressed in a joint conditional model. The three main novelties with respect to related approaches are (1) the interscale-ratios of wavelet coefficients are statistically characterized and different local criteria for distinguishing useful coefficients from noise are evaluated, (2) a joint conditional model is introduced, and (3) a novel anisotropic Markov random field prior model is proposed. The results demonstrate an improved denoising performance over related earlier techniques.     

基于小波域统计建模及显著性修正的SAR图像相干斑抑制

该文提出了一种基于小波域统计建模与小波系数显著性修正相结合的斑点噪声滤波方法。这种方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,对对数变换后的图像进行小波变换并对小波域的高频子带系数用混合高斯模型与隐马尔可夫树模型进行建模,并采用EM算法来估计模型参数。在模型参数估计的基础上;利用贝叶斯最小均方误差准则来估计干净的小波系数。在此基础上引入基于显著性准则的小波系数修正,最后通过小波逆变换与指数变换获得抑制斑点噪声后的图像。用真实SAR图像实验表明,该文提出的方法能够有效地抑制斑点噪声,同时能够很好地保存边缘细节结构与强散射中心。     

基于CHMM 的尖锐频率局部化Contourlet 域图像去噪

提出了一种基于上下文信息隐马尔科夫模型（CHMM）的尖锐频率局部化Contourlet 域图像去噪方法。首先,对噪声图像进行循环平移操作,再利用尖锐频率局部化Contourlet 变换对平移后的图像进行分解,解决了原始Contourlet 变换频率非局部化及缺乏平移不变性的问题,抑制图像在奇异点处产生的伪吉布斯现象。然后,设计一种新的上下文构造方案,针对图像高频子带系数构建CHMM 进行去噪处理。最后,执行尖锐频率局部化Contourlet 逆变换以及逆向循环平移操作获得最终的去噪图像。文中方法采用有效的变换机制并利用上下文信息构建了一个全面的统计相关模型,充分表达了轮廓波高频子带系数在尺度间的持续性、尺度内的多方向选择性和空间邻域内的能量聚集特性,更加有利于图像的去噪处理。实验结果表明:该方法在提高去噪图像PSNR 值的同时进一步改善了其视觉效果,去噪性能优于基于小波变换和原始Contourlet 变换的去噪方法。     

基于四元数小波变换域统计模型的图像去噪

图像去噪是图像处理的基本问题,四元数小波变换是1种新的多尺度分析工具.图像经四元数小波变换后,其小波系数不仅在尺度间具有相关性,而且在尺度内也具有一定的相关性.首先利用层内及层间的相关性,用非高斯分布对四元数小波系数进行建模,然后给出分类准则,把小波系数分类为重要系数和不重要系数,再用非高斯分布模型对重要系数与其邻域系...     

Image denoising with anisotropic bivariate shrinkage

This paper presents a novel image denoising algorithm based on the modeling of wavelet coefficients with an anisotropic bivariate Laplacian distribution function. The anisotropic bivariate Laplacian model not only captures the child-parent dependency between wavelet coefficients, but also fits the anisotropic property of the variances of wavelet coefficients in different scales of natural images. With this statistical model, we derive a closed-form anisotropic bivariate shrinkage function in the framework of Bayesian denoising and a new image denoising approach with local marginal variance estimation based on this newly derived shrinkage function is proposed in the discrete wavelet transform (DWT) domain. The proposed anisotropic bivariate shrinkage approach is also extended to the dual-tree complex wavelet transform (DT-CWT) domain to further improve the performance of image denoising. To take full advantage of DT-CWT, a more accurate noise variance estimator is proposed and the way the anisotropic bivariate shrinkage function applied to the magnitudes of DT-CWT coefficients is presented. Experiments were carried out in both the DWT and the DT-CWT domain to validate the effectiveness of the proposed method. Using a representative set of standard test images corrupted by additive white Gaussian noise, the simulation results show that the proposed method provides promising results and is competitive with the best wavelet-based denoising results reported in the literature both in terms of peak signal-to-noise ratio (PSNR) and in visual quality.     

基于NSCT域各向异性双变量萎缩图像去噪

提出了一种用各向异性双变量拉普拉斯函数模型去模拟NSCT域的系数的图像去噪算法,这种各向异性双边拉普拉斯模型不仅考虑了NSCT系数相邻尺度间的父子关系,同时满足自然图像不同尺度间NSCT系数方差具有各向异性的特征,基于这种统计模型,文中先推导出了一种各向异性双变量收缩函数的近似形式,然后基于贝叶斯去噪法和局部方差估计将这种新的阈值收缩函数应用于NSCT域,实验结果表明文中提出的方法同小波域BiShrink算法、小波域ProbShrink算法、小波域NeighShrink算法相比,能够有效地去除图像的高斯噪声,提高了图像的峰值信噪比;并较完整地保持了图像的纹理和边缘等细节信息,从而明显改善了图像的视觉效果。     

Video denoising via spatially adaptive coefficient shrinkage and threshold adjustment in Surfacelet transform domain

Many video denoising methods originated from methods designed for processing static two-dimensional images. Videos would be processed frame by frame, a process with a relatively high computational complexity, without taking into account the correlation information between frames. In this paper, a video denoising method using coefficient shrinkage and threshold adjustment based on Surfacelet transform (CSTA-ST) is proposed, which processes multiple frames of a video as an ensemble. Spatial correlation is used to define a weighted spatial energy. Each Surfacelet transform (ST) coefficient has a corresponding estimated energy value, in which the ST coefficients are grouped by. The similarity of the ST coefficients in a group determines the threshold of each ST coefficient. In addition, according to the neighborhood information of ST coefficients, the threshold is adjusted by a threshold adjustment factor. The coefficient shrinkage parameter is determined based on the adjusted threshold, and the ST coefficients are shrunk. Finally, the denoised video is obtained by the inverse ST using the shrunk coefficients. In experiments, video sequences with noise are tested, and the denoised results of the proposed method are compared with that of current denoising methods. The experimental results show that the proposed method significantly improves the peak signal-to- noise ratio (PSNR) and the structural similarity (SSIM) for various levels of noise and motion, and the ideal denoised visual effect is obtained.     

一种新的变分去噪模型定参方法

以全变分去噪模型为例,从梯度下降法解相应的欧拉方程着手,提出一种新的定参方法对图像进行去噪,根据新方法选取的参数,同时保证均值和方差估计式。且求解偏微分方程选取的初值不是噪声图像而是对噪声图像进行小波分解后,保留低频系数,只对高频系数设置阈值,再重构后的图像。根据新选取的初值对相应的偏微分方程进行差分迭代求解,数值仿真结果表明,该方法选取的初值具有更好的去噪效果。