A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques.     

Multifocus noisy image fusion using contourlet transform

Abstract

Image fusion is a challenging area of research with a variety of applications. The process of image fusion collects information from different sources and combines them in a single composite image. The composite fused image can better describe the scene than any of the source images. In this paper, we have proposed a method for noisy image fusion in contourlet domain. The proposed method works equally well for fusion of noise free images. Contourlet transform is a multiscale, multidirectional transform with various aspect ratios. These properties make it more suitable for image fusion than other conventional transforms. In the proposed work, the fusion algorithm is combined with a denoising algorithm to reverse the effect of noise. In the proposed method, we have used a level dependent threshold that is based on standard deviation of contourlet coefficients, mean and median of the absolute contourlet coefficients. Experimental results demonstrate that the proposed method performs well in the presence of different types of noise. Performance of the proposed method is compared with principal components analysis and sharp fusion based methods as well as other fusion methods based on variants of wavelet transform like dual tree complex wavelet transform, discrete wavelet transform, lifting wavelet transform, multiwavelet transform, stationary wavelet transform and pyramid transform using six standard quantitative quality metrics (entropy, standard deviation, edge strength, fusion factor, sharpness and peak signal to noise ratio). The combined qualitative and quantitative evaluation of the experimental results shows that the proposed method performs better than other methods.     

Wavelet processing of images for target detection

This article has as its goal the development and test and evaluation of wavelet-based algorithms for automatically detecting unknown anomalies in two-dimensional images. The general idea behind the work is that the class of wavelet transforms induces a so-called multiresolution analysis (MRA) in image space whereby the image of interest is naturally decomposed into a sequence of images of varying resolution (from coarse to fine resolution) in a computationally efficient manner. The anomaly detection can then be performed sequentially beginning at a coarse scale (low resolution) and proceeding to finer scales as needed. The wavelet representation thus effectively allows the user to zoom in on particular areas of interest and thus detect image anomalies in a very efficient manner. The article includes results from computer simulations testing the proposed approach against a standard energy-detection algorithm for the unknown anomalies embedded in additive Gaussian white noise. © 1996 John Wiley & Sons, Inc.     

Electric field induced local features in the photoresponse spectra of CdZnTe crystals

t is established that the photocurrent spectrum I = f(λ) of a CdZnTe crystal depends on the configuration and frequency of an external electric field applied to the sample. In particular, the application of periodic field pulses in the Haar wavelet form leads to modification of the spectrum at frequencies corresponding to certain maxima of the wavelet coefficients. It is demonstrated that, by representing the photore-sponse spectrum in the form of parametric signatures plotted as I(λ) versus dI/dλ, it is possible to separate and evaluate the partial contributions of various photosensitive centers in the given crystal.     

Uniqueness and reciprocal theorems in linear micropolar electro-magnetic thermoelasticity with two relaxation times

A general model for the linear micropolar electro-magnetic thermoelastic continuum based on the hyperbolic heat equation, which is physically more relevant than the classical thermoelasticity theory in analyzing problems involving very short intervals of time and/or very high heat fluxes, is introduced. An integral identity that involves two admissible processes at different instants is established. Uniqueness theorem is proved, with no definiteness assumption on the elastic constitutive coefficients and no restrictions on the electro-elastic coupling moduli, magneto-elastic coupling moduli, and thermal coupling coefficients other than symmetry conditions. The reciprocity theorem is derived, without the use of Laplace transforms. The integral representation formula is obtained in case instantaneous concentrated, time-continuous or time-harmonic loads are applied. The Maysel’s, Somigliana’s and Green’s formulas are derived. The mixed boundary value problem is considered and a system of five singular Fredholm integral equations is obtained. The results for dynamic classical coupled theory can be easy deduced from the given general model formulated for the temperature-rate dependent thermoelasticity.     

连续小波变换快速带通滤波实现算法的研究

针对连续小波变换(Continuous wavelet transform,CWT)计算量较大的问题,提出了一种利用带通滤波实现CWT的快速算法.根据CWT的定义,可将某一尺度下的小波函数看作一带通滤波器的传递函数,于是对信号和小波函数分别进行采样后,再利用快速傅里叶变换实现两个序列的线性卷积,便能求出相应尺度下的小波系数.推导了任意尺度对应的频率与尺度、小波中心频率、信号采样频率、小波函数采样频率之间的关系式,并分析了算法的计算量.仿真和应用结果表明,该法速度很快,精度较高,并且尺度可以任意取值,非常适合于工程应用.     

一种采用多个小波基的图像融合去噪方法

提出了一种新的基于多个小波基的图像融合去噪方法.首先利用多个不同的小波基对含噪图像进行阈值去噪,得到多幅恢复图像.然后对这些图像采用小波融合方法进行融合.对于低频系数采用基于边缘的融合算法,在多幅恢复图像中选择最有可能是边缘的点加以保留;对于高频系数,采用了平均的融合算法.最后得到一幅去噪图像.实验结果表明,无论是在视觉效果上还是在峰值信噪比定量指标上该方法去噪效果均明显优于单一小波基去噪.     

ECG steganography based on tunable Q‐factor wavelet transform and singular value decomposition

The article presents a novel ECG steganography scheme based on the tunable Q‐factor wavelet transformation (TQWT) and also singular value decomposition (SVD) techniques that ensure better safety and confidentiality of patient information. Initial parameters such as Q, r, and J are used to decompose the cover signal into individual frequency sub‐bands with the tunable Q‐factor wavelet transform (TQWT). The singular value decomposition (SVD) technique is used to further decompose high‐frequency sub‐band coefficients into singular values. The watermark information is then embedded with high‐frequency sub‐band coefficients by involving the quantization process. The performance of this proposed system is successfully evaluated by considering various metrics, such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), percentage residual difference (PRD), and bit error rate (BER). The simulation results of the proposed scheme are observed to be better than other traditional algorithms.     

Representation-theoretical properties of the approximate quantum Fourier transform

Among the transformations used in quantum computing the discrete Fourier transform (DFT) plays a key role. A striking fact is that the computational complexity of the DFT with respect to the quantum gate model is polylogarithmic in the length of the input data. In this paper we consider approximate Fourier transformations which are obtained by pruning the twiddle factors of DFT. A parameter is used which determines the level of pruning. The extreme cases are no pruning, which leads to the DFT, and complete pruning, which leads to the Hadamard transform. Our main result is to give a representation-theoretical interpretation of the transformation obtained for all intermediate levels of pruning. We show that the resulting approximate quantum Fourier transforms are basefield transformations, i. e., they decompose the regular representation of the cyclic group over non-splitting fields. Thomas Beth passed away on August 17, 2005. The results presented here had been obtained in joint work with him and had been included into the first author’s Ph.D. thesis. These results have not been published yet.     

Improving performance of wavelet-based image de-noising algorithm using complex diffusion process

Abstract

Image enhancement and de-noising is an essential pre-processing step in many image processing algorithms. In any image de-noising algorithm, the main concern is to keep the interesting structures of the image. Such interesting structures often correspond to the discontinuities (edges). In this paper, we present a new algorithm for image noise reduction based on the combination of complex diffusion process and wavelet thresholding. In the existing wavelet thresholding methods, the noise reduction is limited, because the approximate coefficients containing the main information of the image are kept unchanged. Since noise affects both the approximate and detail coefficients, the proposed algorithm for noise reduction applies the complex diffusion process on the approximation band in order to alleviate the deficiency of the existing wavelet thresholding methods. The algorithm has been examined using a variety of standard images and its performance has been compared against several de-noising algorithms known from the prior art. Experimental results show that the proposed algorithm preserves the edges better and in most cases, improves the measured visual quality of the de-noised images in comparison to the existing methods known from the literature. The improvement is obtained without excessive computational cost, and the algorithm works well on a wide range of different types of noise.     

Edge preserving filter of side scan sonar images with wavelet modulus maxima shift‐correlative technique

An edge preserving filter algorithm of side scan sonar (SSS) image based on wavelet modulus maxima shift‐correlative (WMMS) technique is proposed in this article. First, the proposed WMMS algorithm decomposes SSS image into multiscale wavelet coefficients. Then the modulus maxima, which are produced by catastrophe points, are extracted from wavelet coefficients. The algorithm matches these maxima across the different scales to identify signal or noise. After correcting the “drifting” phenomenon of modulus maxima, a correlation factor array of wavelet coefficients is constructed by strengthening the maxima dominated by signal and suppressing those maxima dominated by noise. By correlating wavelet coefficients with the correlation factor array, the WMMS strengthens the useful high‐frequency signal and weakens the noise. Finally, the algorithm restores SSS image from revised wavelet coefficients. We apply the WMMS algorithm to filter SSS images of the experimental sea areas. Results show that WMMS has advantages over traditional algorithms in suppressing noise and preserving useful high‐frequency information. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 349–355, 2011     

基于连续小波和统计检验的瞬态成分检测与应用

将连续小波变换和统计检验结合用于检测信号中具有一定时频分布的瞬态成分，提出了一种基于连续小波变换和统计检验的瞬态成分检测方法，并用于圆锥滚子轴承振动中的瞬态成分的检测与提取，比较有效地检测出瞬态成分，并在此基础上应用连续小波变换反演得到瞬态成分的估计，表示轴承的状态特征，作为轴承故障诊断的依据。     

A reversible data hiding method without rounding based on Haar wavelet transform: NON-R

The techniques that allow to obtain hidden data and recover the image has recently been much sought after. The basic objective of the method provided in this study was to analyse coefficients of digital images achieved by wavelet transform in the frequency space, to hide data, and to recover original image without loss. Wavelet transform is an efficient method to find pixel coefficient characteristic of digital images. The proposed method (NON-R) hides data using shifted histogram high-frequency Haar coefficients without rounding into integer. Thus, both hidden data and cover image can be easily recovered. Reversibility is provided by retrieving the cover image after hiding into high-frequency wavelet coefficients and extracting the message from wavelet coefficients. Experimental results show that the NON-R has a better performance than its counterparts in terms of statistical and perceptual outcomes.     

Time-Resolved Electrochemical Impedance Spectroscopy of Stochastic Nanoparticle Collision: Short Time Fourier Transform versus Continuous Wavelet Transform

This work demonstrates the utilization of short-time Fourier transform (STFT), and continuous wavelet transform (CWT) electrochemical impedance spectroscopy (EIS) for time-resolved analysis of stochastic collision events of platinum nanoparticles (NPs) onto gold ultramicroelectrode (UME). The enhanced electrocatalytic activity is observed in both chronoamperometry (CA) and EIS. CA provides the impact moment and rough estimation of the size of NPs. The quantitative information such as charge transfer resistance (R_ct) relevant to the exchange current density of a single Pt NP is estimated from EIS. The CWT analysis of the phase angle parameter is better for NP collision detection in terms of time resolution compared to the STFT method.     

基于小波变换的傅里叶加密印刷水印算法研究

在研究傅里叶加密全息技术、数字水印技术和小波理论的基础上,提出了一种基于小波变换的傅里叶加密印刷水印算法。利用该算法重建水印,有效地抑制了载体图像的低频分量对水印产生的噪声的影响;使能量分布在图像的所有像素上,具有良好的抗裁切能力;算法采用密钥加强了水印的安全性。实验表明:嵌入水印的图像一次印刷可检测,二次扫描印刷提取的水印脆弱,可以抵抗基本的图像处理操作。     

Almost perfect binary functions

The almost perfect binary sequences have been defined in [6] as (–1, +1)-periodic sequences such that all their out-of-phase autocorrelation coefficients are zero except one. In the preceding paper, the study of the almost perfect binary sequences is done by means of the ringF ₂[X]/(X ⁿ –1). Here, the arithmetic of cyclotomic fields enables us to solve open problems and questions like: structure and existence of these sequences.     

An efficient and high quality medical CT image enhancement algorithm

Aiming at the process of medical diagnosis, many problems such as unclear images and low contrast are often caused by noise and interference in the process of medical image acquisition and transmission. This article proposes a new image enhancement method that combines the wavelet domain with the spatial domain. First, we input two identical images (Both of the identical images are original images.) in which the first image is enhanced by histogram equalization. Then, the two images are divided into four sub-images by a two-dimensional wavelet transform. The average of the low-frequency coefficients of the low-frequency sub-images of the two images is taken as the low-frequency coefficients of the final reconstruction. Second, aiming at the problem that the contrast may be too low, the fourth high-frequency sub-image is blurred (sharpened) twice. The fourth high-frequency sub-image after blurring is denoised by median filtering. Finally, the four sub-images are fused to obtain the enhanced image. The experimental results show that the peak signal-to-noise ratio, structural similarity, and processing time of the proposed algorithm are better than those of other contrast algorithms, especially the processing time. These objective indicators show that the proposed algorithm can not only effectively suppress noise but also significantly enhance the contrast. Subjectively, compared with other algorithms, the proposed algorithm achieves a better visual effect and greatly reduces the processing time.     

Wavelet-based distortion measure for binary images

Abstract

This paper proposes an objective distortion measure for binary images. Whereas most previously proposed measures decide the distortion weight of a distorted pixel based on the relationship with the neighbouring pixels in the spatial domain, we observe the characteristics of distorted pixels in multi-spectral spatial-frequency bands. Similarities between the binary wavelet transform and the human visual system are utilised to obtain the distortion weight. When we decide the distortion weight of a distorted pixel, we consider flatness, edge orientation and texture complexity of the distorted pixel at each level of binary wavelet decomposition. Experimental results on various sample images show that the proposed measure is more adequate to human than previously proposed measures.     

Texture Analysis for Flaw Detection in Ultrasonic Images

In this paper, we present two approaches for flaw detection in TOFD (Time of Flight Diffraction) images based on texture features. Texture is one of the most important features used in recognizing patterns in an image. The paper describes texture features by two methods: Multiresolution analysis such as wavelet transforms and Gabor filters bank. The two-dimensional wavelet transform is used to decompose the input image into a multiresolution framework. The textural statistical parameters are used to allow the choice of the decomposition channel. The Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave. All Gabor filters can be generated from one mother wavelet by dilation and rotation. These filters represent an appropriate choice for tasks requiring simultaneous measurement in both space and frequency domains. The most relevant features are optimized by Principal Components Analysis (PCA) and used as input data on a Fuzzy C-Mean clustering classifier. We use two classes: ‘defects’ or ‘no defects’. The proposed approach is tested on the TOFD image achieved in an industrial field.     

基于Contourlet变换尺度间相关的图像去噪

Contourlet域数据分析表明,信号的变换域系数在尺度间相关性高,而白噪声则呈弱相关或不相关。通过相关性强弱区分噪声与信号系数,并结合阈值函数,提出了基于Contourlet变换尺度间相关的图像去噪新算法。实验结果表明,新方法去噪后的图像比小波相关去噪算法的PSNR值更高,视觉效果更好,尤其适用于纹理轮廓丰富的图像去噪。