Visual enhancement of digital ultrasound images using multiscale wavelet domain

Ultrasonography has been considered as one of the most powerful techniques for imaging organs and soft tissue structures in the human body. The main disadvantage of medical ultrasonography is the poor quality of images, which are affected by multiplicative speckle noise. In this paper, we present a novel method for despeckling medical ultrasound images. The primary goal of speckle reduction is to remove the speckle without losing much detail contained in an image. To achieve this goal, we make use of the wavelet transform and apply multi-resolution analysis to localize an image into different frequency components or useful subbands and then effectively reduce the speckle in the subbands according to the local statistics within the bands. The main advantage of the wavelet transform is that the image fidelity after reconstruction is visually lossless. The objective of the paper is to investigate the proper selection of wavelet filters and thresholding schemes which yields optimal visual enhancement of ultrasound images, in particular. We employ the wavelet shrinkage denoising techniques with different wavelet bases and decomposition levels on the individual subbands to achieve the best acceptable speckle reduction while maintaining the fidelity of the image and also examine the effects of different thresholding techniques as well as shrinkage rules for denoising ultrasound images. The proposed method consists of the log transformed original ultrasound image being subjected to wavelet transform, which is then denoised by a thresholding technique using a shrinkage rule. Experimental results show that the subband decomposition of ultrasound images, using Bior6.8 and level 3 with soft thresholding based on Bayes shrinkage rule, performs better than other techniques. The performance is measured in terms of Variance, Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), Peak SNR (PSNR) and Correlation Coefficient (CC). The results of wavelet shrinkage techniques are compared with common speckle filters. We observe that the proposed method achieves better visual enhancement of ultrasound images which would lead to more accurate image analysis by the medical experts.     

一种基于小波变换的红外图像去噪方法

提出一种基于小波变换的红外图像去噪方法。该方法针对红外图像的噪声分布特性,对红外图像中的乘性噪声进行对数变换,使乘性噪声变为加性噪声,并对变换后红外图像的小波变换系数进行阈值处理实现图像去噪。实验结果表明:此方法比传统的小波变换方法对噪声有更好的抑制作用。     

基于快速离散曲波变换的图像去噪算法

Curvelet变换可以更好地表示曲线奇异函数的异向性及图像边缘,因此更适合于多尺度图像去噪。针对传统阈值法存在的不足,在分析wrapping方法的快速离散曲波变换基础上,提出结合Cycle Spinning循环平移方法的菱形块阈值规则去噪法,并自适应地对不同的Curvelet子块进行阈值化。该方法可以消除由于Curvelet变换缺乏平移不变性而产生的图像失真,并且更好地利用曲波系数的相关性。实验结果表明,该方法与传统的小波去噪、曲波硬阈值去噪、曲波软阈值去噪、曲波软硬阈值折中法去噪相比,使得去噪图像的峰值信噪比更高,视觉效果更好。     

改进阈值与尺度间相关的小波红外图像去噪

为了更有效地去除红外图像中的噪声, 提出一种基于改进阈值与尺度间相关的小波红外图像去噪方法. 一方面利用阈值修正方案和新阈值函数对通常的小波阈值去噪法进行改进; 另一方面通过对阈值邻近的小波系数进行小波变换尺度间相关性估计, 提高小波系数阈值判断的准确性.实验结果表明, 与通常的小波阈值去噪法相比,该算法能更有效地去除红外图像中的噪声, 获得更高的峰值信噪比(Peak signal-to-noise ratio, PSNR)、边缘保持指数(Edge preserved index, EPI)和更好的视觉效果,具有较好的实用性.     

Nonlocal Image and Movie Denoising

Neighborhood filters are nonlocal image and movie filters which reduce the noise by averaging similar pixels. The first object of the paper is to present a unified theory of these filters and reliable criteria to compare them to other filter classes. A CCD noise model will be presented justifying the involvement of neighborhood filters. A classification of neighborhood filters will be proposed, including classical image and movie denoising methods and discussing further a recently introduced neighborhood filter, NL-means. In order to compare denoising methods three principles will be discussed. The first principle, “method noise”, specifies that only noise must be removed from an image. A second principle will be introduced, “noise to noise”, according to which a denoising method must transform a white noise into a white noise. Contrarily to “method noise”, this principle, which characterizes artifact-free methods, eliminates any subjectivity and can be checked by mathematical arguments and Fourier analysis. “Noise to noise” will be proven to rule out most denoising methods, with the exception of neighborhood filters. This is why a third and new comparison principle, the “statistical optimality”, is needed and will be introduced to compare the performance of all neighborhood filters. The three principles will be applied to compare ten different image and movie denoising methods. It will be first shown that only wavelet thresholding methods and NL-means give an acceptable method noise. Second, that neighborhood filters are the only ones to satisfy the “noise to noise” principle. Third, that among them NL-means is closest to statistical optimality. A particular attention will be paid to the application of the statistical optimality criterion for movie denoising methods. It will be pointed out that current movie denoising methods are motion compensated neighborhood filters. This amounts to say that they are neighborhood filters and that the ideal neighborhood of a pixel is its trajectory. Unfortunately the aperture problem makes it impossible to estimate ground true trajectories. It will be demonstrated that computing trajectories and restricting the neighborhood to them is harmful for denoising purposes and that space-time NL-means preserves more movie details.     

The optimized wavelet filters for speech compression

In this paper, optimized wavelet filters for speech compression are proposed whose wavelet filter coefficients are derived with different window techniques such as Kaiser and Blackman windows via simple linear optimization. When the developed wavelet filters are exploited for speech compression, they not only give better compression ratio but also yield good fidelity parameters as compared to other wavelet filters. A comparative study of performance of different existing wavelet filters and the proposed wavelet filters is made in terms of compression ratio (CR), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and normalized root-mean square error (NRMSE) at different thresholding levels. The simulation result included in this paper shows increased efficacy and improved performance of the proposed filters in the field of speech signal processing.     

改进的双变量收缩函数模型脑电信号消噪方法

针对传统小波消噪全局阈值处理独立性假设和双变量函数模型对没有父系数的最高层小波系数不做处理的缺陷,提出一种高密度离散小波变换中利用双变量收缩函数对脑电信号进行消噪的方法。子小波系数根据双变量函数实现局部自适应收缩处理。同时根据父系数趋于0时,阈值函数近似于软阈值函数,对最高尺度小波系数进行软阈值法消噪。从实际信号处理效果和客观定量指标两方面进行评价,结果表明这种改进算法都优于软阈值法、硬阈值法以及双变量收缩法。     

基于双局部阈值小波收缩的图像去噪算法

提出了一种基于双局部阈值的小波收缩的图像去噪算法。该算法利用小波系数的幅值、空间特性以及对噪声图像的分割,得到两个局部阈值：幅度阈值和空间阈值。利用这两个局部阈值（每个区域阈值不同）对小波系数做相应的“收缩”处理和重构,从而得到一个优质的去噪图像。该算法计算简单速度快,去噪效果明显,优于其他一些去噪算法。     

基于改进小波阈值法的图像去噪算法

图像去噪是图像处理中最基本、最重要的前期工作,本文提出一种基于衰减法的Garrote阈值函数,并将基于该改进阈值函数的小波阈值法用于图像去噪过程,最后通过MATLAB仿真实验验证了本文所提出算法的有效性.本文在分析小波阈值法对图像去噪效果影响的基础上,针对该去噪算法在去除噪声的同时也损失了一定量的图像细节信息的问题,改进了传统阈值函数未考虑阈值以下的小波系数可能含有图像细节信息而对阈值以下小波系数盲目置零的缺点,对Garrote阈值函数阈值以下的小波系数采取衰减方法,以保留更多的图像细节信息,并加入三个调整因子以提高其性能和灵活度,实验表明本文提出的改进小波阈值去噪算法能够有效地去除噪声,且能够保留大量的图像边缘及细节信息.     

基于小波域加权阈值的图像去噪方法

针对小波全局阈值去噪的缺点,介绍了一种子带自适应的阈值加权算法。通过对图像小波分解系数统计特性的分析,提出了一种近指数模型作为分解层之间小波系数的先验分布。在此基础上,对比噪声图像和无噪图像在各尺度下统计特性,给出了一种子带自适应的加权阈值计算方法,避免了各层子带去噪的不平衡。实验表明,与全局阈值和其它子带自适应阈值去噪方法相比,基于加权阈值的图像去噪方法能获得更高的信噪比和更好的视觉效果。     

平移不变的小波域近邻系数阈值MR图像去噪

传统的小波域阈值去噪方法是根据每个小波系数各自的幅度大小进行相应的阈值修正,没有考虑到尺度间以及尺度内近邻的小波系数与当前小波系数的相关性,而使信号得不到更准确的估计。根据信号和噪声在尺度间的不同传播特性和尺度内近邻小波系数的相关性,设计出一种平移不变（TI）的近邻系数阈值策略,并依据磁共振成像（MRI）噪声图像的特点,结合复数域统一体去噪方法,提出了一种新颖的基于平移不变的小波域近邻系数阈值MR图像去噪算法。实验表明该算法能更准确地估计信号,且与几种磁共振（MR）图像去噪算法相比具有更好的去噪效果。     

基于小波分析的图像去噪处理

近年来,小波理论得到了非常迅速的发展,而且由于其具备良好的时频特性,实际应用也非常广泛。这里希望利用小波的自身特性,在降低噪声影响的同时,尽量保持图像本身的有用细节和边缘信息,从而保证图像的最佳效果。其中图像的小波阈值去噪方法可以说是众多图像去噪方法的佼佼者。     

小波去噪中软硬阈值的一种改良折衷法

在小波域中分析了传统软阈值法和硬阈值法的特点,并在标准软硬阈值折衷法的基础上,提出了一种软硬阈值改良折衷法.与标准软硬阈值折衷法相比,其阈值函数具有更加灵活多变的形式,便于进行各种数学处理;与传统软阈值法和硬阈值法相比,它克服了硬阈值函数不连续的缺点,减小了软阈值函数中的估计小波系数与分解小波系数之间存在的恒定偏差.仿真实验结果表明,该改良方法的去噪性能优于传统软阈值法、硬阈值法、标准软硬阈值折衷法及一些现有的新阈值函数法.     

基于空域相关的小波域硬阈值滤波

论文提出一种新的基于小波域相关性的自适应阈值滤波算法。首先构造了一种基于空域相关系数的硬阈值函数,通过极小化该阈值函数对应的SURE估计得到均方差意义下自适应于信号和噪声的最优阈值。应用该阈值作用于小波域的空域相关系数,以达到去除噪声的目的。仿真实验表明,该方法可得到很好的滤波效果。     

An adaptive edge-preserving image denoising technique using patch-based weighted-SVD filtering in wavelet domain

Image denoising has always been one of the standard problems in image processing and computer vision. It is always recommendable for a denoising method to preserve important image features, such as edges, corners, etc., during its execution. Image denoising methods based on wavelet transforms have been shown their excellence in providing an efficient edge-preserving image denoising, because they provide a suitable basis for separating noisy signal from the image signal. This paper presents a novel edge-preserving image denoising technique based on wavelet transforms. The wavelet domain representation of the noisy image is obtained through its multi-level decomposition into wavelet coefficients by applying a discrete wavelet transform. A patch-based weighted-SVD filtering technique is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method achieves very impressive gain in denoising performance.     

一种新的具有增强效果的小波域图像去噪方法

为了使去噪后的图像具有更佳的视觉效果,基于新近出现的一种小波域阈值去噪方法——NeighShrink,提出了一种具有细节增强效果的小波域图像去噪方法——增强型邻域收缩方法(enhanced NeighShrink,ENS)。该方法一方面继承了NeighShrink方法的优点,在对小波系数进行阈值处理时,由于考虑了其与邻域系数的相关性,从而大大减少了误判图像细节为噪声的情况,同时,通过改变NeighShrink方法中小波系数收缩因子的计算方法,用该方法去噪后的图像取得了高于NeighShrink方法的峰值信噪比;另一方面,通过引入一个细节增强因子P,使得该方法能够对图像细节进行增强,从而得到了更佳的视觉效果。通过实验证明,该方法能够在去噪和细节增强这两方面优于普通软阈值去噪方法和NeighShrink方法。     

A new wavelet-based fuzzy single and multi-channel image denoising

In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This feature space distinguishes between important coefficients, which belong to image discontinuity and noisy coefficients. We use this fuzzy feature for enhancing wavelet coefficients' information in the shrinkage step. Then a fuzzy membership function shrinks wavelet coefficients based on the fuzzy feature. In addition, we extend our noise reduction algorithm for multi-channel images. We use inter-relation between different channels as a fuzzy feature for improving the denoising performance compared to denoising each channel, separately. We examine our image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Extensive comparisons with the state-of-the-art image denoising algorithm indicate that our image denoising algorithm has a better performance in noise suppression and edge preservation.     

基于边缘检测的Contourlet变换图像去噪

为了实现较好保留图像边缘特征的有效去噪,提出了一种基于边缘检测的Contourlet变换去噪方法。该方法先用LOG算子提取图像边缘,进而在Contourlet变换域上对图像的边缘部分和非边缘部分分别选取不同阈值进行最佳软阈值去噪处理。实验表明,与采用Donoho软阈值的Contourlet变换去噪方法相比,该方法可有效地保留图像的边缘信息,达到了更好的去噪效果。     

新小波阈值函数在医学图像去噪中的应用

为了提高小波阈值去噪算法中的软阈值和硬阈值以及已有改进阈值函数存在的不足,提出了新的分层阈值函数的方法。该算法首先对噪声图像进行分解,从而得出小波系数。然后用改进的阈值函数对高频部分系数进行分层阈值处理。最后根据所得估计的小波系数在小波基的条件下,对图像进行重构,得到去噪后图像。该阈值函数具有优良的数学特性,通过对医学图像仿真实验结果表明,该算法去噪的效果无论是在视觉效果上,还是在均方差和信噪比性能分析上均优于常用的阈值函数,所以该算法在解决实际去噪问题中值得推广与应用。     

基于提升小波MRI图像自适应阈值去噪算法

分析了小波的消失矩特性对图像重构误差的影响,提出了利用提升算法提高双正交小波消失矩的改进算法。通过提升算法对传统小波提高消失矩,改善了小波的性能,使小波具有更好的振荡性,能够更好地捕捉图像的细节,从而提高了重构信号的精确度。根据磁共振图像的特点及其噪声的分布特性,提出了一种对小波系数进行分块处理的阈值去噪方法。通过对分解后每个层次上的各高频系数矩阵分为多个子矩阵分别进行不同阈值的选取,实现在不同的对比度区域选取不同的阈值的目的,从而使阈值的选取更具有自适应性。