基于改进中值滤波和提升小波变换的阈值去噪方法研究

对于实际拍摄的一些图像信噪比低,噪声密度大,且含有混合噪声,而现有算法大多只能去除单一噪声的问题。针对混合噪声中含有的脉冲噪声和高斯噪声,提出基于改进中值滤波和提升小波变换去噪相结合的方法。去噪过程中,使用中值滤波器提取脉冲噪声并采用中值滤波算法滤波后,构造提升小波,采用改进阈值函数提升小波阈值去噪方法去除高斯噪声。实验结果表明,当噪声值(,)=(0.4, 20)时,采用本文去噪方法,峰值信噪比（PSNR）为34.002 1,平均绝对误差（MAE）为2.365 3。     

小波域高斯混合模型与中值滤波的混合图像去噪研究

基于高斯混合模型的小波去噪方法并结合中值滤波法对脉冲噪音有较好滤除效果的特点,将这两种方法结合起来,对含有高斯脉冲混合噪音图像进行去噪处理.该算法采用Matlab语言进行仿真.实验结果表明,这种混合去噪方法的效果要优于单纯使用中值滤波或者小波去噪的效果.     

基于非局部均值和SUSAN算子的混合噪声滤除

为了更好地滤除图像中脉冲噪声和高斯噪声组成的混合噪声,提出了一种基于非局部均值和Small Univalue Segment Assimilating Nucleus(SUSAN)算子的混合噪声滤除方法.该方法首先根据脉冲噪声点与角点之间吸收核同值区形状特征的不同,采用SUSAN算子检测出大量的特征点,特征点主要是脉冲噪声点,也可能含有小部分角点.将特征点进行排序,出现频次最高两位的点为脉冲噪声点.然后采用改进的均值滤波法计算脉冲噪声点邻域中非脉冲噪声点的均值,以此替换脉冲噪声点灰度值.最后针对已滤除脉冲噪声的图像,采用考虑了图像块信息的非局部均值方法滤除剩余的高斯噪声.去噪实验结果表明:与自适应中值和加权均值结合的方法、中值滤波与小波结合的方法、脉冲耦合神经网络与中值滤波结合的方法相比,本文方法主观视觉效果更好,能够更好地保留图像中的边缘细节,客观评价指标峰值信噪比有较大的提高,滤除混合噪声的优势明显.     

自适应中值-加权均值混合滤波器

为了去除图像中混入的脉冲噪声和高斯噪声,提出了一种基于自适应中值滤波和自适应加权均值滤波的混合滤波方法。该方法先将图像分为若干区域,并对每个区域进行噪声检测以实现两类噪声的分离,然后再分别采用自适应中值滤波和自适应加权均值滤波将分离出的脉冲噪声和高斯噪声去除。对这种新方法进行了计算机模拟实验。结果表明:新方法较前人提及的三种混合滤波方法具有更优的滤波性能,在有效抑制混合噪声的同时能很好地保护图像中的细节,为消除图像中的混合噪声提供了一种有效的途径。     

基于小波变换的图像混合噪声自适应滤除算法

为同时滤除图像中的椒盐噪声和高斯噪声,提出了一种基于小波变换的混合噪声自适应滤除算法,该算法首先采用中值滤波去除椒盐噪声,然后借助边缘检测算子区将图像为分边缘与非边缘区域,进一步对非边缘区域引入改进的均值滤波器,有效削弱高斯噪声的同时保护图像边缘细节,既初步削弱高斯噪声又保护了边缘,最后采用改进的小波阈值滤波算法,对不同的小波系数采用不同的阈值函数,通过线性回归得到各最优阈值关系式。实验结果表明,该混合噪声自适应滤除算法能有效滤除椒盐噪声和高斯噪声,在图像主观质量和客观质量上均取得了较好的效果,能提高去噪图像峰值信噪比0.5~2.0 dB。     

层析三维数字化仪层析图像滤波器的设计及研究

提出一种新型的递归中值滤波器，抛掉了统计参数的制约，将滤波算法转化为一种优化处理。该方法兼顾了滤波处理的光滑连续性及抑制噪声的累积特性，可有效地消除脉冲型干扰的影响，同时也从理论的角度上对该算法进行了分析。为消除加性高斯噪声，提出了一种基于图像边缘方向的小波线性滤波器，它仅仅处理边缘信息。该算汉的极大优点是克服了边缘模糊效应，小波的去噪逆向重构的处理方法对边缘为跃型的层析图象非常实用。     

基于中值预滤波的航空图像小波去噪算法研究

结合航空图像的噪声来源与图像特性,提出一种基于中值预滤波的图像小波去噪算法.图像首先经中值滤波器进行预滤波,滤除随机的脉冲式噪声,然后对处理后的图像进行小波变换,与给定阈值相比,对可明显判为信号或噪声的系数进行相应处理;对不确定为信号或噪声的系数进行多尺度上的相关性追溯,判别其归属后进行处理.实验结果表明:该方法客观上提高了图像的信噪比,主观上使去噪后的图像纹理分明,能更好地适合人眼的视觉特性,有利于航空图像的分析、判读.     

改进的中值滤波算法在图像去噪中的应用

 针对标准中值滤波方法存在的不足,结合均值思想提出两种改进的中值滤波算法,即加权快速中值滤波算法和加权自适应中值滤波算法,MATLAB实验证实两种方法均能更好地保存原始图像的细节和边缘。比较两种新方法得出以下结论：加权改进中值滤波算法对低密度的脉冲噪声去噪效果明显,对于高密度脉冲噪声去噪效果不理想,但能大大提高中值滤波的运行速度,对数字图像实时处理意义很大;加权自适应中值滤波算法能够有效地消除被污染图像中的高密度脉冲噪声,较标准中值滤波具有更优良的滤波性能,较加权快速中值滤波算法在去噪方面有更好的鲁棒性。     

基于粗糙集与小波变换的图像融合算法

粗糙集理论是处理不确定性问题的数学方法，本文提出了基于粗糙集与小波变换相结合的图像融合算法。该方法首先将粗糙集理论应用于图像滤波中，对含有椒盐噪声的图像进行粗糙中值滤波，然后对滤波后的图像进行小波融合。实验结果表明，粗糙中值滤波有较强的去噪能力，且较好地保持了图像的细节信息，在此基础上进行小波融合，使得融合结果图像具有良好的效果。     

基于小波变换的压缩感知理论对水质检测紫外-可见光谱数据的去噪研究

消除噪声影响对提高直接光谱法水质检测系统的测量稳定性和精度都具有重要意义。直接光谱法在线水质检测系统通常采用长寿命、无需预热的脉冲氙灯和适用于复杂检测环境的工业级光谱探测装置。针对整个光谱探测系统受到光源、光路和光电转换器件的严重影响,测定的光谱数据含有大量噪声这一实际问题,提出了基于小波变换的压缩感知去噪算法,并与传统小波阈值去噪方法进行了比较实验。针对化学需氧量为200 mg·L-1的邻苯二甲酸氢钾标液的紫外-可见光谱数据进行去噪处理,采用压缩感知去噪算法,将信号在小波域内分解,得到含噪高频系数;采用随机高斯矩阵作为压缩感知算法的观测矩阵,压缩比设置为2,对高频系数进行观测;选择正交匹配追踪算法恢复高频小波系数的稀疏性,从而达到去噪目的。同时针对传统的小波阈值去噪,采用daubechies4作为小波基的软阈值滤波方法对光谱数据进行去噪处理。为验证去噪算法的可行性,采集某溪水和城市生活污水的光谱信号分别采用以上两种方法进行去噪处理,实验结果表明：基于小波变换的压缩感知去噪算法适用于紫外-可见光谱法在线水质检测系统,该方法能在保留水样原始光谱信号的吸收特征的前提下有效地去噪,且去噪效果优于小波阈值去噪算法。与小波阈值去噪算法相比,信噪比提高了12.201 5 dB,均方根误差减小了0.009 3,峰值信噪比增加了5.299 dB。不仅避免了小波阈值去噪过程中阈值的选取依靠主观判断问题,而且在重构过程中有效地抑制了噪声,为直接光谱法检测水质参数提供了一种新的解决方案。     

基于邻域特征匹配的通用噪声滤波器

图像去噪是遥感图像复原的重要步骤。在去除图像噪声的同时希望尽可能多地保留图像的纹理细节信息。受较差的成像环境和图像数据远距离传输的影响,遥感图像中一般都含有较强的高斯-脉冲混合噪声,而在现有的图像去噪算法中,能够同时去除图像中的高斯-脉冲混合噪声的通用噪声滤波器很少。以非局部平均方法的滤波思想为基础,通过引入邻域相似度评价的概念和脉冲噪声探测器,提出了基于邻域特征匹配的通用噪声滤波器。实验结果表明:基于邻域特征匹配的通用噪声滤波器具备有很好地去除图像高斯-脉冲混合噪声的能力,在去除高斯-脉冲混合噪声的同时能够很好地保持图像的复杂纹理和精细细节,并且便于向DSP/FPGA多处理器平台上移植。     

基于提升小波的红外图像去噪算法与实现

介绍了提升小波原理,以及把提升小波应用于红外图像去噪的算法.研究了提升小波的硬件实现方法,根据FPGA器件具有快速逻辑处理能力的特点,采用流水线的加法及桶状移位操作指令,设计了一种适合FPGA实现的提升小波图像去噪的硬件结构.实验结果表明,以FPGA来实现基于小波变换的图像去噪方法与传统方法相比,在速度方面得到了很大的提高.     

基于混沌粒子群优化的图像Contourlet阈值去噪

提出了基于混沌粒子群优化的图像Contourlet阈值去噪方法.该方法在Contourlet变换域内利用混沌粒子群算法来确定最优阈值,再通过软阈值函数去噪,且不需要噪音方差等先验信息.实验结果表明：该方法与小波Bayeshrink阈值、基于粒子群的小波阈值、Contourlet自适应阈值等去噪方法相比,能有效地去除高斯白噪音和椒盐噪音的混合噪音,提高峰值信噪比,并较好地保留图像的细节和纹理,从而明显地改善了图像的视觉效果.     

Wavelet domain de-noising of time-courses in MR image sequences

Magnetic resonance images acquired with high temporal resolution often exhibit large noise artifacts, which arise from physiological sources as well as from the acquisition hardware. These artifacts can be detrimental to the quality and interpretation of the time-course data in functional MRI studies. A class of wavelet-domain de-noising algorithms estimates the underlying, noise-free signal by thresholding (or 'shrinking') the wavelet coefficients, assuming the underlying temporal noise of each pixel is uncorrelated and Gaussian. A Wiener-type shrinkage algorithm is developed in this paper, for de-noising either complex- or magnitude-valued image data sequences. Using the de-correlation properties of the wavelet transform, as elucidated by Johnstone and Silverman, the assumption of i.i.d. Gaussian noise can be abandoned, opening up the possibility of removing colored noise. Both wavelet- and wavelet-packet based algorithms are developed, and the Wiener method is compared to the traditional Hard and Soft wavelet thresholding methods of Donoho and Johnstone. The methods are applied to two types of data sets. In the first, an artificial set of complex-valued images was constructed, in which each pixel has a simulated bimodal time-course. Gaussian noise was added to each of the real and imaginary channels, and the noise removed from the complex image sequence as well as the magnitude image sequence (where the noise is Rician). The bias and variance between the original and restored paradigms was estimated for each method. It was found that the Wiener method gives better balance in bias and variance than either Hard or Soft methods. Furthermore, de-noising magnitude data provides comparable accuracy of the restored images to that obtained from de-noising complex data. In the second data set, an actual in vivo complex image sequence containing unknown physiological and instrumental noise was used. The same bimodal paradigm as in the first data set was added to pixels in a small localized region of interest. For the paradigm investigated here, the smooth Daubechies wavelets provide better de-noising characteristics than the discontinuous Haar wavelets. Also, it was found that wavelet packet de-noising offers no significant improvement over the computationally more efficient wavelet de-noising methods. For the in vivo data, it is desirable that the groups of "activated" time-courses are homogeneous. It was found that the internal homogeneity of the group of time-courses increases when de-noising is applied. This suggests using de-noising as a pre-processing tool for both exploratory and inferential data analysis methods in fMRI.     

便携式X光机图像降噪复合算法研究

便携式X光机应用越来越广,因而对其所成图像的降噪也显得尤为重要.本文首先讨论和分析了便携式X光机成像系统噪声的来源及其特性,针对传统单一降噪算法存在的不足,提出了一种新的复合式降噪算法.该复合算法不仅对高斯噪声和脉冲噪声有很好的滤除效果,同时也能有效地保护图像的细节部分.仿真实验证明,该算法切实可行.     

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR approximately 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak's method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.