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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Hybrid semi-blind image watermarking in redundant wavelet domain

Multimedia Tools and Applications - Image watermarking in wavelet domain has been found useful for copyright protection and rightful ownership. Classical wavelet transforms, like discrete wavelet...     

Fast multiresolution image operations in the wavelet domain

A wide class of operations on images can be performed directly in the wavelet domain by operating on coefficients of the wavelet transforms of the images and other matrices defined by these operations. Operating in the wavelet domain enables one to perform these operations progressively in a coarse-to-fine fashion, operate on different resolutions, manipulate features at different scales, trade off accuracy for speed, and localize the operation in both the spatial and the frequency domains. Performing such operations in the wavelet domain and then reconstructing the result is also often more efficient than performing the same operation in the standard direct fashion. In this paper, we demonstrate the applicability and advantages of this framework to three common types of image operations: image blending, 3D warping of images and sequences, and convolution of images and image sequences.     

Edge detection by scale multiplication in wavelet domain

This paper proposes a wavelet based edge detection scheme by scale multiplication. The dyadic wavelet transforms at two adjacent scales are multiplied as a product function to magnify the edge structures and suppress the noise. Unlike many multiscale techniques that first form the edge maps at several scales and then synthesize them together, we determined the edges as the local maxima directly in the scale product after an efficient thrsholding. It is shown that the scale multiplication achieves better results than either of the two scales, especially on the localization performance. The dislocation of neighboring edges is also improved when the width of detection filter is set large to smooth noise. Experiments on natural images are compared with the Laplacian of Gaussian and Canny edge detection algorithms.     

小波域中的鲁棒性盲水印算法

提出了自适应的鲁棒性图像水印方案。用密钥选择嵌入的位置,增加水印的安全性,并只对有嵌入要求的图像块进行离散小波变换,这样算法的计算复杂度只与水印容量有关,与宿主图像无关,相对减少了计算的复杂度。在水印嵌入过程中,对相应的有嵌入要求的图像块进行分类来决定不同的嵌入强度,达到了自适应的嵌入要求。为了更好地增加方案的鲁棒性,采用基因算法对阈值进行了优化。实验结果表明,此方案具有很好的透明性,对通常的图像处理具有很好的鲁棒性。     

MIMO-OFDM系统中基于小波包的信道估计方法

对多输入多输出-正交频分复用(MIMO-OFDM)系统中基于导频辅助的最小二乘(LS)信道估计算法进行研究,针对LS算法对噪声影响比较敏感的缺点,提出了一种基于小波包去噪的信道估计方法,对导频符号的信道响应进行去噪处理后,再做内插估计.根据该方法的思想,基于长期演进(LTE)协议进行计算机仿真与分析,结果表明该方法比传统的LS估计算法具有更好的性能,能够有效减小信道噪声的影响,提高信道估计精度.     

A wavelet domain detail compensation filtering technique for InSAR interferograms

The quality of interferogram filtering affects the accuracy of interferometric synthetic aperture radar (InSAR) applications. This article presents a new wavelet domain filtering method for phase noise reduction in an InSAR interferogram. The method first transforms the real and imagery parts of the original interferogram into the wavelet domain using the stationary wavelet transform (SWT). Then the coefficients for each sub-band are filtered with detail compensation. Finally, the wavelet coefficients are reconstructed in the space domain by the inverse SWT. The results show that the proposed method can suppress the speckle effectively, maintain details of the interferogram well, and greatly reduce the number of residues.     

Feature extracted from wavelet eigenfunction estimation for text-independent speaker recognition

A new speaker feature extracted from wavelet eigenfunction estimation is described. The signal is decomposed through interpolating the scaling function. Wavelets can offer a significant computational advantage by reducing the dimensionality of the eigenvalue problem. Our results have shown that this wavelet feature introduced better performance than the other Karhunen-Loeve transform (KLT) with respect to the percentages of recognition.