Hiding digital watermarks using multiresolution wavelet transform

In this paper, an image accreditation technique by embedding digital watermarks in images is proposed. The proposed method for the digital watermarking is based on the wavelet transform. This is unlike most previous work, which used a random number of a sequence of bits as a watermark and where the watermark can only be detected by comparing an experimental threshold value to determine whether a sequence of random signals is the watermark. The proposed approach embeds a watermark with visual recognizable patterns, such as binary, gray, or color image in images by modifying the frequency part of the images. In the proposed approach, an original image is decomposed into wavelet coefficients. Then, multi-energy watermarking scheme based on the qualified significant wavelet tree (QSWT) is used to achieve the robustness of the watermarking. Unlike other watermarking techniques that use a single casting energy, QSWT adopts adaptive casting energy in different resolutions. The performance of the proposed watermarking is robust to a variety of signal distortions, such as JPEG, image cropping, sharpening, median filtering, and incorporating attacks     

基于整数小波变换的Wiener插值算法

小波变换中高低分辨率子带之间的相似性使得利用小波变换进行图像插值的方法成为可能.根据数字图像信号的特点,分析了小波变化后各个子带信号的特点,提出了基于整数小波变换的Wiener插值算法.用Wiener自适应滤波器训练得到插值滤波系数,同时结合既符合图像性质又能减小运算量的整数双正交小波基对图像插值.结果得到较高的信噪比和较好的主观视觉效果.平均峰值信噪比比传统的双线性插值法提升了2.4dB.     

基于小波变换修正的双线性插值图像放大方法

首先分析小波双线性插值的图像放大算法优缺点,然后根据小波分解各部分之间也具有相似性提出了一种改善的被小波变换修正的双线性插值算法,其仿真结果显示放大后图像在细节丰富程度上不如前者,但视觉效果上改善明显.     

Interpolation and extrapolation using a high-resolution discreteFourier transform

We present an iterative nonparametric approach to spectral estimation that is particularly suitable for estimation of line spectra. This approach minimizes a cost function derived from Bayes' theorem. The method is suitable for line spectra since a “long tailed” distribution is used to model the prior distribution of spectral amplitudes. Since the data themselves are used as constraints, phase information can also be recovered and used to extend the data outside the original window. The objective function is formulated in terms of hyperparameters that control the degree of fit and spectral resolution. Noise rejection can also be achieved by truncating the number of iterations. Spectral resolution and extrapolation length are controlled by a single parameter. When this parameter is large compared with the spectral powers, the algorithm leads to zero extrapolation of the data, and the estimated Fourier transform yields the periodogram. When the data are sampled at a constant rate, the algorithm uses one Levinson recursion per iteration. For irregular sampling, the algorithm uses one Cholesky decomposition per iteration. The performance of the algorithm is illustrated with three different problems that arise in geophysical data: (1) harmonic retrieval from a time series contaminated with noise; (2) linear event detection from a finite aperture array of receivers, (3) interpolation/extrapolation of gapped data. The performance of the algorithm as a spectral estimator is tested with the Kay and Marple (1981) data set     

Noise-robust pitch detection method using wavelet transform with aliasing compensation

An improved wavelet-based method is developed for extracting pitch information from noisy speech. It uses a modified spatial correlation function which is originally applied to wavelet-based signal denoising to improve the performance of pitch detection in a noisy environment. The modified spatial correlation function needed in the proposed pitch detection method makes use of an aliasing compensation algorithm to eliminate the aliasing distortion that arises from the downsampling and upsampling operations of the wavelet transform. As a consequence, this allows one to further increase the accuracy of pitch detection. It is shown in various experimental results that this new method gives a considerable performance improvement when compared with other conventional and wavelet-based methods.     

Surface alignment of an elastic body using a multiresolution wavelet representation

An algorithm for nonlinear registration of an elastic body is developed. Surfaces (outlines) of known anatomic structures are used to align all other (internal) points. The deformation field is represented with a multiresolution wavelet expansion and is modeled by the partial differential equations of linear elasticity. A hierarchical approach that reduces algorithm complexity is adopted. The performance of the algorithm is evaluated by two-dimensional alignment of sections from mouse brains located in the olfactory bulbs. The registration algorithm was guided by manually delineated contours of a subset of brain structures and validated based on another subset of brain structures. The wavelet alignment algorithm produced a twofold to fivefold improvement in accuracy over an affine (linear) alignment algorithm.     

Motion estimation using a complex-valued wavelet transform

This paper describes a new motion estimation algorithm that is potentially useful for both computer vision and video compression applications. It is hierarchical in structure, using a separable two-dimensional (2-D) discrete wavelet transform (DWT) on each frame to efficiently construct a multiresolution pyramid of subimages. The DWT is based on a complex-valued pair of four-tap FIR filters with Gabor-like characteristics. The resulting complex DWT (CDWT) effectively implements an analysis by an ensemble of Gabor-like filters with a variety of orientations and scales. The phase difference between the subband coefficients of each frame at a given subpel bears a predictable relation to a local translation in the region of the reference frame subtended by that subpel. That relation is used to estimate the displacement field at the coarsest scale of the multiresolution pyramid. Each estimate is accompanied by a directional confidence measure in the form of the parameters of a quadratic matching surface. The initial estimate field is progressively refined by a coarse-to fine strategy in which finer scale information is appropriately incorporated at each stage. The accuracy, efficiency, and robustness of the new algorithm are demonstrated in comparison testing against hierarchical implementations of intensity gradient-based and fractional-precision block matching motion estimators     

Multiscale video representation using multiresolution motioncompensation and wavelet decomposition

A multiscale video representation using wavelet decomposition and variable-block-size multiresolution motion estimation (MRME) is presented. The multiresolution/multifrequency nature of the discrete wavelet transform makes it an ideal tool for representing video sources with different resolutions and scan formats. The proposed variable-block-size MRME scheme utilizes motion correlation among different scaled subbands and adapts to their importance at different layers. The algorithm is well suited for interframe HDTV coding applications and facilitates conversions and interactions between different video coding standards. Four scenarios for the proposed motion-compensated coding schemes are compared. A pel-recursive motion estimation scheme is implemented in a multiresolution form. The proposed approach appears suitable for the broadcast environment where various standards may coexist simultaneously     

Rotation-invariant multiresolution texture analysis using radon and wavelet transforms.

A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A kappa-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.     

Classifying mammographic mass shapes using the wavelet transform modulus-maxima method

In this article, multiresolution analysis, specifically the discrete wavelet transform modulus-maxima (mod-max) method, is utilized for the extraction of mammographic mass shape features. These shape features are used in a classification system to classify masses as round, nodular, or stellate. The multiresolution shape features are compared with traditional uniresolution shape features for their class discriminating abilities. The study involved 60 digitized mammographic images. The masses were segmented manually by radiologists, prior to introduction to the classification system. The uniresolution and multiresolution shape features were calculated using the radial distance measure of the mass boundaries. The discriminating power of the shape features were analyzed via linear discriminant analysis (LDA). The classification system utilized a simple Euclidean metric to determine class membership. The system was tested using the apparent and leave-one-out test methods. The classification system when using the multiresolution and uniresolution shape features resulted in classification rates of 83% and 80% for the apparent and leave-one-out test methods, respectively. In comparison, when only the uniresolution shape features were used, the classification rates were 72 and 68% for the apparent and leave-one-out test methods, respectively.     

Signal decomposition of transmembrane voltage-sensitive dye fluorescence using a multiresolution wavelet analysis

Fluorescence imaging of transmembrane voltage-sensitive dyes is used to study electrical activation in cardiac tissue. However, the fluorescence signals, typically, have low SNRs and may be contaminated with motion artifact. In this report, we introduce a new processing approach for fluoresced transmembrane potentials (fTmps) that is based upon a discrete wavelet transform. We show how fTmp signals can be decomposed and reconstructed to form three subsignals that contain signal noise (noise signal), the early depolarization phase of the action potential (rTmp signal), and motion artifact (rMA signal). A coiflet4 wavelet is used for fTmp decomposition and reconstruction of these subsignals. Results using fTmp signals that are contaminated with motion artifact indicate that the approach is a useful processing step to remove baseline drift, reduce noise, and reveal wavefronts. It streamlines the preprocessing of fTmps for the subsequent measurement of activation times and conduction velocities. It is a promising approach for studying wavefronts without aggressive mechanical tissue constraint or electromechanical uncoupling agents and is, useful for single-camera systems that do not provide for ratiometric imaging.     

Image compression using wavelet transform and multiresolutiondecomposition

Schemes for image compression of black-and-white images based on the wavelet transform are presented. The multiresolution nature of the discrete wavelet transform is proven as a powerful tool to represent images decomposed along the vertical and horizontal directions using the pyramidal multiresolution scheme. The wavelet transform decomposes the image into a set of subimages called shapes with different resolutions corresponding to different frequency bands. Hence, different allocations are tested, assuming that details at high resolution and diagonal directions are less visible to the human eye. The resultant coefficients are vector quantized (VQ) using the LGB algorithm. By using an error correction method that approximates the reconstructed coefficients quantization error, we minimize distortion for a given compression rate at low computational cost. Several compression techniques are tested. In the first experiment, several 512x512 images are trained together and common table codes created. Using these tables, the training sequence black-and-white images achieve a compression ratio of 60-65 and a PSNR of 30-33. To investigate the compression on images not part of the training set, many 480x480 images of uncalibrated faces are trained together and yield global tables code. Images of faces outside the training set are compressed and reconstructed using the resulting tables. The compression ratio is 40; PSNRs are 30-36. Images from the training set have similar compression values and quality. Finally, another compression method based on the end vector bit allocation is examined.     

基于小波分析的倒装芯片主动红外缺陷检测

随着微电子技术的需求和发展,倒装芯片技术在高密度微型化封装领域得到了快速发展和广泛应用,而现有的一些倒装芯片检测方法存在一定的不足之处。为此,研究了主动红外的倒装芯片缺陷检测方法。实验中使用激光加热对倒装样片施加非接触热激励,通过红外热像仪获取样片温度分布。采用小波分析方法提取包括小波熵在内的信号特征,采用自组织神经网络对不同类型焊球进行聚类识别。研究表明,通过自组织神经网络可以有效地将不同缺陷焊球与参考焊球通过距离映射法映射到不同区域从而区分开,并且可以将未知焊球信号映射到相应的区域实现聚类识别。因此该方法可以有效实现倒装芯片的缺陷检测。     

Image analysis using a dual-tree M-band wavelet transform.

We propose a two-dimensional generalization to the M-band case of the dual-tree decomposition structure (initially proposed by Kingsbury and further investigated by Selesnick) based on a Hilbert pair of wavelets. We particularly address: 1) the construction of the dual basis and 2) the resulting directional analysis. We also revisit the necessary pre-processing stage in the M-band case. While several reconstructions are possible because of the redundancy of the representation, we propose a new optimal signal reconstruction technique, which minimizes potential estimation errors. The effectiveness of the proposed M-band decomposition is demonstrated via denoising comparisons on several image types (natural, texture, seismics), with various M-band wavelets and thresholding strategies. Significant improvements in terms of both overall noise reduction and direction preservation are observed.     

ECG compression using non-recursive wavelet transform with quality control

While wavelet-based electrocardiogram (ECG) data compression using scalar quantisation (SQ) yields excellent compression performance, a wavelet’s SQ scheme, however, must select a set of multilevel quantisers for each quantisation process. As a result of the properties of multiple-to-one mapping, however, this scheme is not conducive for reconstruction error control. In order to address this problem, this paper presents a single-variable control SQ scheme able to guarantee the reconstruction quality of wavelet-based ECG data compression. Based on the reversible round-off non-recursive discrete periodised wavelet transform (RRO-NRDPWT), the SQ scheme is derived with a three-stage design process that first uses genetic algorithm (GA) for high compression ratio (CR), followed by a quadratic curve fitting for linear distortion control, and the third uses a fuzzy decision-making for minimising data dependency effect and selecting the optimal SQ. The two databases, Physikalisch-Technische Bundesanstalt (PTB) and Massachusetts Institute of Technology (MIT) arrhythmia, are used to evaluate quality control performance. Experimental results show that the design method guarantees a high compression performance SQ scheme with statistically linear distortion. This property can be independent of training data and can facilitate rapid error control.     

Realization of wavelet transform using switched-current filters

A method for realizing wavelet transform (WT) is presented, in which the WT is synthesized by a bank of switched-current (SI) filters whose impulse responses are the basic wavelet function and its dilations. SI circuits are well suitable for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the sampling frequency. In this article, the wavelet base is approximated by a systematic algorithm with all the involved approximation parameters taken into account. Also, the SI filter employing the follow-the-leader feedback (FLF) multiple-loop feedback (MLF) structure is proposed to synthesize the approximation function. The Gaussian wavelet is selected as an example to illustrate the design procedure. Simulation results indicate that the proposed method has the merits of high approximation accuracy, strong stability and low sensitivity.     

小波变换帧间预测医学图像编码

该文报道了一种新的医学图像编码方法,称为具有运动补偿的小波变换帧间预测医学图像编码。模拟结果表明:这种新算法比现有的同类算法有更好的性能。     

Image coding using wavelet transform and classified vectorquantisation

The wavelet transform, which provides a multiresolution representation of images, has been widely used in image compression. A new image coding scheme using the wavelet transform and classified vector quantisation is presented. The input image is first decomposed into a hierarchy of three layers containing ten subimages by the discrete wavelet transform. The lowest resolution low frequency subimage is scalar quantised with 8 bits/pixel. The high frequency subimages are compressed by classified vector quantisation to utilise the crosscorrelation among different resolutions while reducing the edge distortion and computational complexity. Vectors are constructed by combining the corresponding wavelet coefficients of different resolutions in the same orientation and classified according to the magnitude and the position of wavelet transform coefficients. Simulation results show that the proposed scheme has a better performance than those utilising current scalar or vector quantisation schemes     

Zero-crossings of a wavelet transform

The completeness, stability, and application to pattern recognition of a multiscale representation based on zero-crossings is discussed. An alternative projection algorithm is described that reconstructs a signal from a zero-crossing representation, which is stabilized by keeping the value of the wavelet transform integral between each pair of consecutive zero-crossings. The reconstruction algorithm has a fast convergence and each iteration requires O( N log² (N)) computation for a signal of N samples. The zero-crossings of a wavelet transform define a representation which is particularly well adapted for solving pattern recognition problems. As an example, the implementation and results of a coarse-to-fine stereo-matching algorithm are described     

Video watermarking using wavelet transform and tensor algebra

We present a robust, hybrid non-blind MPEG video watermarking technique based on a high-order tensor singular value decomposition and the discrete wavelet transform (DWT). The core idea behind our proposed technique is to use the scene change analysis to embed the watermark repeatedly into the singular values of high-order tensors computed form the DWT coefficients of selected frames of each scene. Experimental results on video sequences are presented to illustrate the effectiveness of the proposed approach in terms of perceptual invisibility and robustness against attacks.