基于小波去噪与变换域的信道估计方法

针对长期演进(LTE)下行正交频分复用(OFDM)系统的最小二乘(LS)信道估计算法对噪声比较敏感的问题,提出了一种基于小波变换去噪与变换域插值相结合的信道估计方法.该方法通过在最小二乘(LS)估计之后加入小波阈值去噪过程,再通过变换域低通滤波插值估计进行双重去噪处理.计算机仿真结果表明,该估计方法能够有效地去除加性高斯白噪声,比一般的LS估计算法性能要好,在一定程度上弥补了LS估计算法对噪声敏感的缺陷.     

平稳小波域深度残差CNN用于低剂量CT图像估计

针对低剂量计算机断层扫描（LDCT）重建图像中存在大量噪声的问题,提出了一种平稳小波的深度残差卷积神经网络（SWT-CNN）模型,可以从LDCT图像估计标准剂量计算机断层扫描（NDCT）图像。该模型在训练阶段,将LDCT图像经平稳小波（SWT）三级分解后的高频系数作为输入,将LDCT图像高频系数与NDCT图像高频系数相减得到残差系数作为标签,通过深度卷积神经网络（CNN）学习输入和标签之间的映射关系;在测试阶段,利用此映射关系即可从LDCT图像的高频系数中预测NDCT高频系数,最后通过平稳小波反变换（ISWT）重构预测的NDCT图像。实验采用50对大小为512×512的同一体模的常规剂量胸腔及腹腔扫描切片和投影域添加噪声后的重建图像作为数据集,其中45对作为训练集,其余5对作为测试集。将所提模型与效果较好的非局部降噪算法、K-奇异值分解（K-SVD）算法、匹配三维滤波（BM3D）算法及图像域CNN（Image-CNN）模型对比,实验结果表明,SWT-CNN模型预测的NDCT图像信噪比（PSNR）和结构相似性（SSIM）高,且均方根误差（RMSE）小于其他算法处理结果。该模型对于提高低剂量CT图像质量是可行且有效的。     

基于混沌的图像小波域加密算法

研究了适应用于JPEG2000标准的图像加密技术。首先给出了一个基于Chebychev多项式的混沌映射，分析了它的统计特性；为实现基于质量可控的图像加密，构建了加密质量控制模型，并在此基础上进一步构造了图像小波域加密算法，对算法进行了有效性分析。实验结果验证了该算法的可靠性、安全性和高效性，而且对JPSEC是实用的。     

Using wavelet network in nonparametric estimation

Wavelet networks are a class of neural networks consisting of wavelets. In this paper, algorithms for wavelet network construction are proposed for the purpose of nonparametric regression estimation. Particular attentions are paid to sparse training data so that problems of large dimension can be better handled. A numerical example on nonlinear system identification is presented for illustration.     

Efficient and secure cryptosystem for fingerprint images in wavelet domain

In this paper, a fingerprint image encryption algorithm is proposed in order to enhance the protection of fingerprint-based systems against replay attacks. The proposed algorithm is consisting of permutation and diffusion operations in wavelet domain, whereas, one-level Lifting Wavelet Transform Integer-to-Integer is performed to the original fingerprint image. The approximation and detail sub-bands are then partitioned into blocks and permuted using a permutation key. It is noteworthy that, for each sub-band the Rubik’s cube principle is applied. The encrypted image is constructed by ordering the encrypted sub-bands. Eventually, an experimental tests and security analysis were conducted on three fingerprint images attained through Fingerprint Verification Competition “FVC 2000” database. The obtained results confirm the effectiveness of the proposed encryption algorithm and clearly show the robustness against common attacks, for example differential and statistical attacks. In addition, it reveals the high security level achieved.     

Intelligent reversible watermarking in integer wavelet domain for medical images

The prime requirement of reversible watermarking scheme is that the system should be able to restore the cover work to its original state after extracting the hidden information. Reversible watermarking approaches, therefore, have wide applications in medical and defense imagery. In this paper, an intelligent reversible watermarking approach GA-RevWM for medical images is proposed. GA-RevWM is based on the concept of block-based embedding using genetic algorithm (GA) and integer wavelet transform (IWT). GA based intelligent threshold selection scheme is applied to improve the imperceptibility for a fixed payload or vice versa. The experimental results show that GA-RevWM provides significant improvement in terms of imperceptibility for a desired level of payload against the existing approaches.     

一种改进的小波域阈值去噪算法

在D.L.Donoho和I.M Johnston提出的多分辨分析小波阈值去噪方法的基础上,提出了一种新的双变量阈值函数.采用新的阈值函数的去噪效果无论在视觉效果上,还是在信噪比增益上和最小均方意义上均优于传统的硬阈值和软阈值,克服了采用硬阈值法去噪效果不佳和软阈值法过度光滑使信号失真的缺点.通过仿真实验结果,表明该方法的有效性和优越性.     

Adaptive singular value decomposition in wavelet domain for image denoising

Image denoising is an important issue in image preprocessing. Two popular methods to the problem are singular value decomposition (SVD) and wavelet transform. Various denoising algorithms based on these two methods have been independently developed. This paper proposes an approach for image denoising by performing SVD filtering in detail subbands of wavelet domain, where SVD filtering is adaptive to the inhomogeneous nature of natural images. Comparisons were made with respect to both SVD-based filtering methods and wavelet transform-based methods.     

Block-based image inpainting in the wavelet domain

This paper introduces a new model for block-based image inpainting in the wavelet domain. The proposed technique separates the inpainting process into two different and important steps, both using the energy of wavelet coefficients. First, the model explores wavelet detail coefficients to estimate the image gradient vector, weighting each vector with the energy of wavelet coefficients. Such information is then used to determine which block belonging to the inpainting region should be filled first. After that, an adapted method for texture synthesis in the wavelet domain is applied in order to successfully fill this block. These two steps are applied successively, until the inpainting region is completely filled. Experimental results indicate that the proposed algorithm can fill large inpainting regions with good visual quality, presenting results comparable to or better than other competitive approaches for image inpainting.     

Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images

Ultrasound imaging is widely used for diagnostic purposes among the clinicians. A major problem concerning the ultrasound images is their inherent corruption by the multiplicative speckle noise that hampers the quality of the diagnosis, and reduces the efficiency of the algorithms for automatic image processing. In this paper, we propose a new spatially adaptive wavelet-based method in order to reduce the speckle noise from ultrasound images. A spatially adaptive threshold is introduced for denoising the coefficients of log-transformed ultrasound images. The threshold is obtained from a Bayesian maximum a posteriori estimator that is developed using a symmetric normal inverse Gaussian probability density function (PDF) as a prior for modelling the coefficients of the log-transformed reflectivity. A simple and fast method is provided to estimate the parameters of the prior PDF from the neighbouring coefficients. Extensive simulations are carried out using synthetically speckled and ultrasound images. It is shown that the proposed method outperforms several existing techniques in terms of the signal-to-noise ratio, edge preservation index and structural similarity index and visual quality, and in addition, is able to maintain the diagnostically significant details of ultrasound images.     

Robust gray-scale logo watermarking in wavelet domain

In this paper, a robust logo watermarking scheme based on image fusion is proposed. Unlike existing watermarking schemes, the used watermark is a gray scale logo instead of randomly generated Gaussian noise type watermark. The core idea of the proposed scheme is to decompose an image into frequency sub-bands using wavelet transform followed by the embedding in selected blocks of sub-bands obtained by ZIG-ZAG sequence. Block selection is done by taking variance of the blocks into consideration. The experimental results show better visual imperceptibility and resiliency of the proposed scheme against intentional or un-intentional variety of attacks and superiority is carried out by comparison made by us with the existing schemes.     

小波域红外图像增强算法研究

由于红外图像成像机理及红外成像系统自身的原因,红外图像大多对比度低、细节信息不明显,视觉效果差,需要经过增强处理改善图像质量。提出一种基于小波的多分辨分析方法和Retinex图像增强算法相结合的红外图像增强方法。利用小波把红外图像分解成近似子图像和细节子图像,对近似子图像进行改进的Retinex增强算法处理,对细节子图像采用多策略小波阈值增强,最后小波重构得到增强的红外图像。实验结果表明,该算法对红外图像具有较好的增强效果。     

基于小波域的图像自适应模糊增强

本文结合小波变换和模糊逻辑提出一种新的图像增强方法,利用改进的自适应模糊权重中值滤波滤除噪声,应用模糊增强算子对图像增强.经试验仿真,该方法在去噪的的过程中保留了大部分细节及边缘信息,同时改善了视觉效果.     

Minimum classification error learning for sequential data in the wavelet domain

Wavelet analysis has found widespread use in signal processing and many classification tasks. Nevertheless, its use in dynamic pattern recognition have been much more restricted since most of wavelet models cannot handle variable length sequences properly. Recently, composite hidden Markov models which observe structured data in the wavelet domain were proposed to deal with this kind of sequences. In these models, hidden Markov trees account for local dynamics in a multiresolution framework, while standard hidden Markov models capture longer correlations in time. Despite these models have shown promising results in simple applications, only generative approaches have been used so far for parameter estimation. The goal of this work is to take a step forward in the development of dynamic pattern recognizers using wavelet features by introducing a new discriminative training method for this Markov models. The learning strategy relies on the minimum classification error approach and provides re-estimation formulas for fully non-tied models. Numerical experiments on phoneme recognition show important improvement over the recognition rate achieved by the same models trained using maximum likelihood estimation.     

小波域基于MFA_ICA检测的数字图像水印算法

基于MFA_ICA（Independent Component Analysis based on Mean Field Approximation）理论提出了一种数字图像水印算法。该算法利用MFA_ICA进行水印和原始图像的估计,并用估计出的独立分量检测水印是否存在。与传统线性相关检测器相比,该算法消除了原始载体的干扰,检测性能明显提高。重要的是,不仅通过仿真实验证明了该算法较强抵抗JPEG压缩、剪切和白噪声等攻击的能力,而且从理论上也给出了严格的证明。     

Spatial domain wavelet design for feature preservation in computational data sets

High-fidelity wavelet transforms can facilitate visualization and analysis of large scientific data sets. However, it is important that salient characteristics of the original features be preserved under the transformation. We present a set of filter design axioms in the spatial domain which ensure that certain feature characteristics are preserved from scale to scale and that the resulting filters correspond to wavelet transforms admitting in-place implementation. We demonstrate how the axioms can be used to design linear feature-preserving filters that are optimal in the sense that they are closest in L2 to the ideal low pass filter. We are particularly interested in linear wavelet transforms for large data sets generated by computational fluid dynamics simulations. Our effort is different from classical filter design approaches which focus solely on performance in the frequency domain. Results are included that demonstrate the feature-preservation characteristics of our filters.     

小波变换估计非线性扩散最优停止时间

Gilboa提出一种针对高斯噪声的基于信噪比(SNR)最优的迭代停止时间估计方法。该方法用一个噪声补丁来估计图像噪声与冗余(噪声图像与去噪图像的差)的协方差对冗余方差的导数,补丁是随机生成的纯高斯噪声图像,其均值为零并且方差等于噪声图像的噪声方差。在实际应用中图像噪声方差未知,补丁的噪声是随机的,不同噪声所得到的最后停止时间可能不同。针对这些问题,对该方法进行了改进。首先将图像进行小波变换;再利用小波系数的层间相关性去掉第1层斜向高频系数(HH1)中的边缘纹理信息,获得"纯"的子噪声;然后把子噪声作为补丁的噪声取代随机噪声。实验结果表明,改进方法不仅能解决随机噪声补丁的两个问题,而且去噪图像在峰值信噪比(PSNR)上有一定优势。     

Frequency domain sample maximum likelihood estimation for spatially dependent parameter estimation in PDEs

The identification of the spatially dependent parameters in Partial Differential Equations (PDEs) is important in both physics and control problems. A methodology is presented to identify spatially dependent parameters from spatio-temporal measurements. Local non-rational transfer functions are derived based on three local measurements allowing for a local estimate of the parameters. A sample Maximum Likelihood Estimator (SMLE) in the frequency domain is used, because it takes noise properties into account and allows for high accuracy consistent parameter estimation. Confidence bounds on the parameters are estimated based on the noise properties of the measurements. This method is successfully applied to the simulations of a finite difference model of a parabolic PDE with piecewise constant parameters.     

小波域的快速自适应图像修复算法

传统的基于偏微分方程的图像修复算法需要大量迭代,修复所耗时间较长,复杂度高。针对这一问题,提出了一种小波域的非迭代自适应图像修复算法。该算法对破损图像进行小波分解,找到待修复区域,根据待修复区域及其邻域像素值自适应选择修复模板大小,对修复模板内的像素值进行方向筛选,使修复过程严格按照等照度线方向行进,对修复后的图像进行小波重构。实验结果表明,该方法显著地缩短了修复时间,且对于图像的纹理细节、结构信息都达到了更好的修复效果。