Vector quantisation codebook initialisation for discrete multiwavelet transform

A novel vector quantisation codebook initialisation method called vector component difference is proposed for image coding using the discrete multiwavelet transform. Simulation results show that it is superior to the currently available methods when using with the Linde-Buzo-Gray codebook generation algorithm.     

Fusion algorithm for multisensor images based on discrete multiwavelet transform

The authors review the notion of multiwavelets and describe the use of the discrete multiwavelet transform (DMWT) in image fusion processing. Multiwavelets are extensions from scalar wavelets, and have several advantages in comparison with scalar wavelets. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. A novel fusion algorithm is presented for multisensor images based on the discrete multiwavelet transform that can be performed at pixel level. After the registering of source images, a pyramid for each source image can be obtained by applying decomposition with multiwavelets in each level. The multiwavelet decomposition coefficients of the input images are appropriately merged and a new fused image is obtained by reconstructing the fused multiwavelet coefficients. This image fusion algorithm may be used to combine images from multisensors to obtain a single composite with extended information content. The results of experiments indicate that this image fusion algorithm can provide a more satisfactory fusion outcome.     

The recursive pyramid algorithm for the discrete wavelet transform

The recursive pyramid algorithm (RPA) is a reformulation of the classical pyramid algorithm (PA) for computing the discrete wavelet transform (DWT). The RPA computes the N-point DWT in real time (running DWT) using just L(log N-1) words of storage, as compared with O(N) words required by the PA. L is the length of the wavelet filter. The RPA is combined with the short-length FIR filter algorithms to reduce the number of multiplications and additions     

基于离散小波变换的网络流量多重分形模型

网络流量过程中所蕴含的分形尺度特性对网络性能有显著的影响。因此研究能全面准确地刻画网络流量过程在小时间／空间尺度上的复杂奇异性特征和大时间／空间尺度上的长程依赖性特征的流量模型对Internet网络工程有重要的意义。本文对实测的流量数据(从著名的校园网和国内著名的ISP)进行了分析，利用小波技术构建了一个新的网络流量的多重分形模型，通过模拟验证，发现该新模型能以较简洁的形式捕捉实际网络流量特性，并具有刻画真实流量数据中的多重分形特征的能力。     

The discrete wavelet transform: wedding the a trous and Mallatalgorithms

Two separately motivated implementations of the wavelet transform are brought together. It is observed that these algorithms are both special cases of a single filter bank structure, the discrete wavelet transform, the behavior of which is governed by the choice of filters. In fact, the a trous algorithm is more properly viewed as a nonorthonormal multiresolution algorithm for which the discrete wavelet transform is exact. Moreover, it is shown that the commonly used Lagrange a trous filters are in one-to-one correspondence with the convolutional squares of the Daubechies filters for orthonormal wavelets of compact support. A systematic framework for the discrete wavelet transform is provided, and conditions are derived under which it computes the continuous wavelet transform exactly. Suitable filter constraints for finite energy and boundedness of the discrete transform are also derived. Relevant signal processing parameters are examined, and it is observed that orthonormality is balanced by restrictions on resolution     

Three-dimensional discrete wavelet transform architectures

The three-dimensional (3-D) discrete wavelet transform (DWT) suits compression applications well, allowing for better compression on 3-D data as compared with two-dimensional (2-D) methods. This paper describes two architectures for the 3-D DWT, called the 3DW-I and the 3DW-II. The first architecture (3DW-I) is based on folding, whereas the 3DW-II architecture is block-based. Potential applications for these architectures include high definition television (HDTV) and medical data compression, such as magnetic resonance imaging (MRI). The 3DW-I architecture is an implementation of the 3-D DWT similar to folded 1-D and 2-D designs. It allows even distribution of the processing load onto 3 sets of filters, with each set performing the calculations for one dimension. The control for this design is very simple, since the data are operated on in a row-column-slice fashion. Due to pipelining, all filters are utilized 100% of the time, except for the start up and wind-down times. The 3DW-II architecture uses block inputs to reduce the requirement of on-chip memory. It has a central control unit to select which coefficients to pass on to the lowpass and highpass filters. The memory on the chip will be small compared with the input size since it depends solely on the filter sizes. The 3DW-I and 3DW-II architectures are compared according to memory requirements, number of clock cycles, and processing of frames per second. The two architectures described are the first 3-D DWT architectures     

Direction-adaptive discrete wavelet transform for image compression.

We propose a direction-adaptive DWT (DA-DWT) that locally adapts the filtering directions to image content based on directional lifting. With the adaptive transform, energy compaction is improved for sharp image features. A mathematical analysis based on an anisotropic statistical image model is presented to quantify the theoretical gain achieved by adapting the filtering directions. The analysis indicates that the proposed DA-DWT is more effective than other lifting-based approaches. Experimental results report a gain of up to 2.5 dB in PSNR over the conventional DWT for typical test images. Subjectively, the reconstruction from the DA-DWT better represents the structure in the image and is visually more pleasing.     

FPGA implementation for 2D discrete wavelet transform

An operator correlation-based algorithm and its VLSI architecture For computing the 2D discrete wavelet transform is presented. The proposed discrete wavelet transform architecture was simulated in Verilog and synthesised with the FPGA compiler. The implementation for the 2D discrete wavelet transform on an FPGA-based design style is described     

VLSI implementation of discrete wavelet transform

This paper presents a VLSI implementation of discrete wavelet transform (DWT). The architecture is simple, modular, and cascadable for computation of one or multidimensional DWT. It comprises of four basic units: input delay, filter, register bank, and control unit. The proposed architecture is systolic in nature and performs both high- and low-pass coefficient calculations with only one set of multipliers. In addition, it requires a small on-chip interface circuitry for interconnection to a standard communication bus. A detailed analysis of the effect of finite precision of data and wavelet filter coefficients on the accuracy of the DWT coefficients is presented. The architecture has been simulated in VLSI and has a hardware utilization efficiency of 87.5%. Being systolic in nature, the architecture can compute DWT at a data rate of N×10⁶ samples/s corresponding to a clock speed of N MHz     

基于小波变换的硬拷贝全息水印

提出一种基于离散小波变换的硬拷贝数字全息水印方法。首先利用共轭对称延拓傅里叶计算全息生成水印全息图;然后对其进行小波分解,将包含全部水印信息的高频分量嵌入到载体图像小波分解的中频区域以实现信息隐藏。由于全息图的频谱可控性和不可撕毁性,本文全息水印方法具有很好的不可见性,可以实现水印盲提取,不仅能够抵抗剪切、噪声、滤波和...     

Fast text location based on discrete wavelet transform

The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, the text is fast detected from complex background images stored in the compressed format such as JPEG2000 without full decompress. Compared with some traditional character location methods, the proposed scheme has the advantages of low computational cost, robust to size and font of characters and high accuracy. Preliminary experimental results show that the proposed scheme is efficient and effective.     

Combining the discrete wavelet transform and mixed-domain filtering

A novel filtering method is proposed that combines the discrete orthogonal wavelet transform (DWT) with the mixed-domain (mixed-D) filtering method. The method uses the DWT to pre- and postprocess those dimensions of the signal that are transformed to the discrete-frequency domain by mixed-D filtering. Using the DWT in this manner provides a controlled mechanism to partition the spectrum of the input signal into subband signals, which then may be selectively filtered during the linear difference equation (LDE) step of the mixed-D algorithm. It is shown that, when the DWT is computed using filters with ideal high- and lowpass frequency responses, the LDE filters used in the mixed-D filtering stage are unchanged by the introduction of the DWT (although the frequency tuple associated with each LDE filter is altered). This indicates that the mixed-D filtering scheme can be easily used in subband coding systems. Results are given for the filtering of a three-dimensional (3-D) linear trajectory signal, representing a common application in video processing.     

Reusable silicon IP cores for discrete wavelet transform applications

Architectures and methods for the rapid design of silicon cores for implementing discrete wavelet transforms over a wide range of specifications are described. These architectures are efficient, modular, scalable, and cover orthonormal and biorthogonal wavelet transform families. They offer efficient hardware utilization by exploiting a number of core wavelet filter properties and allow the creation of silicon designs that are highly parameterized, including in terms of wavelet type and wordlengths. Control circuitry is embedded within these systems allowing them to be cascaded for any desired level of decomposition without any interface glue logic. The time to produce chip designs for a specific wavelet application is typically less than a day and these are comparable in area and performance to handcrafted designs. They are also portable across a wide range of silicon foundries and suitable for field programmable gate array and programmable logic data implementation. The approach described has also been extended to wavelet packet transforms.     

Simplified biorthogonal discrete wavelet transform for VLSI architecture design

Biorthogonal discrete wavelet transform (BDWT) has gained general acceptance as an image processing tool. For example, the JPEG2000 standard is completely based on the BDWT. In BDWT, the scaling (low-pass) and wavelet (high-pass) filters are symmetric and linear phase. In this work we show that by using a specific sign modulator the BDWT filter bank can be realized by only two biorthogonal filters. The analysis and synthesis parts use the same scaling and wavelet filters, which simplifies especially VLSI designs of the biorthogonal DWT/IDWT transceiver units. Utilizing the symmetry of the scaling and the wavelet filters we introduce a fast convolution algorithm for implementation of the filter modules. In multiplexer–demultiplexer VLSI applications both functions can be constructed via two running BDWT filters and the sign modulator. This work was supported by the National Technology Agency of Finland (TEKES).     

Image coding using dual-tree discrete wavelet transform

In this paper, we explore the application of 2-D dual-tree discrete wavelet transform (DDWT), which is a directional and redundant transform, for image coding. Three methods for sparsifying DDWT coefficients, i.e., matching pursuit, basis pursuit, and noise shaping, are compared. We found that noise shaping achieves the best nonlinear approximation efficiency with the lowest computational complexity. The interscale, intersubband, and intrasubband dependency among the DDWT coefficients are analyzed. Three subband coding methods, i.e., SPIHT, EBCOT, and TCE, are evaluated for coding DDWT coefficients. Experimental results show that TCE has the best performance. In spite of the redundancy of the transform, our DDWT _ TCE scheme outperforms JPEG2000 up to 0.70 dB at low bit rates and is comparable to JPEG2000 at high bit rates. The DDWT _TCE scheme also outperforms two other image coders that are based on directional filter banks. To further improve coding efficiency, we extend the DDWT to an anisotropic dual-tree discrete wavelet packets (ADDWP), which incorporates adaptive and anisotropic decomposition into DDWT. The ADDWP subbands are coded with TCE coder. Experimental results show that ADDWP _ TCE provides up to 1.47 dB improvement over the DDWT _TCE scheme, outperforming JPEG2000 up to 2.00 dB. Reconstructed images of our coding schemes are visually more appealing compared with DWT-based coding schemes thanks to the directionality of wavelets.     

Enhanced localized computerized tomography employing the discrete wavelet transform

A new localized computerized tomography technique based on the multiresolution analysis (MRA) implementation of the discrete wavelet transform is proposed. Our technique is based upon viewing the projection data as a set of one-dimensional functions of the space variablet and decomposing each one into an approximation signal and a set of detail signals using MRA. The approximation signal and detiil signals associated with each projection are filtered using the ramp filter || of the standard reconstruction technique filtered back projection to generate the set of filtered projections. It is shown that only a very sparse set of projection data outside of the region of interest (ROI) is required to reconstruct a high-quality image of the ROI and a reasonable image outside of the ROI. Simulation results using the Shepp-Logan head phantom are presented to demonstrate the proposed technique.     

Multiscale characterization of chronobiological signals based on the discrete wavelet transform

To compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT, this new approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis.     

VLSI architecture for fast 2D discrete orthonormal wavelet transform

The discrete wavelet transform (DWT) provides a new method for signal/image analysis where high frequency components are studied with finer time resolution and low frequency components with coarser time resolution. It decomposes a signal or an image into localized contributions for multiscale analysis. In this paper, we present a parallel pipelined VLSI array architecture for 2D dyadic separable DWT. The 2D data array is partitioned into non-overlapping groups of rows. All rows in a partition are processed in parallel, and consecutive partitions are pipelined. Moreover, multiple wavelet levels are computed in the same pipeline, and multiple DWT problems can be pipelined also. The whole computation requires a single scan of the image data array. Thus, it is suitable for on-line real-time applications. For anN×N image, anm-level DWT can be computed in time units on a processor costing no more than , whereq is the partition size,p is the length of corresponding 1D DWT filters,C _m andC _a are the costs of a parallel multiplier and a parallel adder respectively, and a time unit is the time for a multiplication and an addition. Forq=N m, the computing time reduces to . When a large number of DWT problems are pipelined, the computing time is about per problem.     

基于小波变换和离散余弦变换的fisher脸识别

本文结合几种现有的人脸识别特征提取算法,先对人脸图像进行小波分解去噪;然后通过离散余弦变换对低频分量作进一步特征提取和压缩,保留人脸图像中对光照、姿态、表情变化不敏感的识别信息;接着利用PCA和LDA相结合得到最终的识别特征;最后采用欧式距离和最近邻分类器识别人脸。实验采用ORL标准人脸库验证了这种组合的有效性。