Inverse halftoning using wavelets

This work introduces a new approach to inverse halftoning using nonorthogonal wavelets. The distinct features of this wavelet-based approach are: (1) edge information in the highpass wavelet images of a halftone image is extracted and used to assist inverse halftoning, (2) cross-scale correlations in the multiscale wavelet decomposition are used for removing background halftoning noise while preserving important edges in the wavelet lowpass image, and (3) experiments show that our simple wavelet-based approach outperforms the best results obtained from inverse halftoning methods published in the literature, which are iterative in nature.     

Tomographic reconstruction using nonseparable wavelets

In this paper, the use of nonseparable wavelets for tomographic reconstruction is investigated. Local tomography is also presented. The algorithm computes both the quincunx approximation and detail coefficients of a function from its projections. Simulation results showed that nonseparable wavelets provide a reconstruction improvement versus separable wavelets.     

Modeling video traffic using wavelets

In this letter we establish a wavelet model for video traffic. Different from the existing methods which model the video traffic in the time domain, we model the wavelet coefficients in the wavelet domain. The strength of the wavelet model includes: (1) an unified approach to model both the long-range and the short-range dependence in the video traffic simultaneously; (2) a computationally efficient method for developing the model and generating high quality video traffic; and (3) feasibility of performance analysis using the model     

Multiresolution tomographic reconstruction using wavelets

Shows how the separable two-dimensional wavelet representation leads naturally to an efficient multiresolution tomographic reconstruction algorithm. This algorithm is similar to the conventional filtered backprojection algorithm, except that the filters are now angle dependent, and the backprojection gives the wavelet coefficients of the reconstruction, which are then used to synthesize the reconstruction at various resolution levels. By reconstructing only a small localized region at high resolution, the authors show how radiation exposure and computation can be significantly reduced, compared to a standard reconstruction.     

Classification of polymeric material surfaces

The surface of polymers and other material has a structure which can be analysed by the fractal approach using the fractal dimension D. This value is related to the physical or chemical treatment of the surface     

Bandwidth efficient PAM signaling using wavelets

In this article, we demonstrate a bandwidth efficient method of pulse amplitude modulation (PAM) signaling using a family of orthonormal wavelets as the baseband pulse. These wavelets can be transmitted using single sideband (SSB) transmission, since they have zero average value. We provide a comparison with raised-cosine signaling, with the wavelet approach offering 50% greater data rate at the same bandwidth     

State-variable-based transient circuit simulation using wavelets

For the first time a state variable transient analysis using wavelets is developed and implemented in a circuit simulator. The formulation is particularly well suited to modeling RF and microwave circuits and is validated by considering a nonlinear transmission line. However, results indicate that still more research is needed to make this method efficient for the simulation of large circuits     

Robust image watermarking using non-regular wavelets

An approach to watermarking digital images using non-regular wavelets is advanced. Non-regular transforms spread the energy in the transform domain. The proposed method leads at the same time to increased image quality and increased robustness with respect to lossy compression. The approach provides robust watermarking by suitably creating watermarked messages that have energy compaction and frequency spreading. Our experimental results show that the application of non-regular wavelets, instead of regular ones, can furnish a superior robust watermarking scheme. The generated watermarked data is more immune against non-intentional JPEG and JPEG2000 attacks.     

Performance analysis of image compression using wavelets

The aim of this paper is to examine a set of wavelet functions (wavelets) for implementation in a still image compression system and to highlight the benefit of this transform relating to today's methods. The paper discusses important features of wavelet transform in compression of still images, including the extent to which the quality of image is degraded by the process of wavelet compression and decompression. Image quality is measured objectively, using peak signal-to-noise ratio or picture quality scale, and subjectively, using perceived image quality. The effects of different wavelet functions, image contents and compression ratios are assessed. A comparison with a discrete-cosine-transform-based compression system is given. Our results provide a good reference for application developers to choose a good wavelet compression system for their application     

Segmentation of bright targets using wavelets and adaptivethresholding

A general systematic method for the detection and segmentation of bright targets is developed. We use the term "bright target" to mean a connected, cohesive object which has an average intensity distribution above that of the rest of the image. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayes classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). A performance analysis based on a Gaussian distribution model is used to show that the obtained adaptive threshold is often close to the Bayes threshold. The method has proven robust even when the image distribution is unknown. Examples are presented to demonstrate the efficiency of the technique on a variety of targets.     

Speed estimation in wireless systems using wavelets

A new technique is described for estimating the speed of a mobile station in a wireless system. The mobile speed maps the characteristic spatial scale of the received signal into a characteristic temporal scale. The continuous wavelet transform tracks changes in the temporal scale to estimate the mobile speed as a function of time. This technique requires neither knowledge of the average received power of the nonstationary signal nor adaptation of a temporal observation window, in contrast to other speed estimators given in the literature. Simulations show the tracking of a variable speed profile     

Combined image compression and denoising using wavelets

This paper presents a novel scheme for simultaneous compression and denoising of images: WISDOW-Comp (Wavelet based Image and Signal Denoising via Overlapping Waves—Compression). It is based on the atomic representation of wavelet details employed in WISDOW for image denoising. However, atoms can be also used for achieving compression. In particular, the core of WISDOW-Comp consists of recovering wavelet details, i.e. atoms, by exploiting wavelet low frequency information. Therefore, just the approximation band and significance map of atoms absolute maxima have to be encoded and sent to the decoder for recovering a cleaner as well as compressed version of the image under study. Experimental results show that WISDOW-Comp outperforms the state of the art of compression based denoisers in terms of both rate and distortion. Some technical devices will also be investigated for further improving its performances.     

Supervised classification of marble textures using support vector machines

A novel approach is described for the supervised classification of marble textures in different classes according to visual appearance, using sum and difference histograms for texture analysis and feature extraction, and support vector machines for classification. Results show very good discrimination between classes.     

Coherent multiscale image processing using dual-tree quaternion wavelets

The dual-tree quaternion wavelet transform (QWT) is a new multiscale analysis tool for geometric image features. The QWT is a near shift-invariant tight frame representation whose coefficients sport a magnitude and three phases: two phases encode local image shifts while the third contains image texture information. The QWT is based on an alternative theory for the 2-D Hilbert transform and can be computed using a dual-tree filter bank with linear computational complexity. To demonstrate the properties of the QWT's coherent magnitude/phase representation, we develop an efficient and accurate procedure for estimating the local geometrical structure of an image. We also develop a new multiscale algorithm for estimating the disparity between a pair of images that is promising for image registration and flow estimation applications. The algorithm features multiscale phase unwrapping, linear complexity, and sub-pixel estimation accuracy.     

Compressing image-based relighting data using eigenanalysis and wavelets

In image-based relighting (IBL) a tremendous number of reference images are needed to synthesise a high-quality novel image. This collection of reference images is referred as an IBL data set. An effective compression method for IBL data makes the IBL technique more practical. Within an IBL data set, there is a strong correlation among different reference images. In conventional eigen-based image compression methods, the principal component analysis (PCA) process is used for exploiting the correlation within a single image. Such an approach is not suitable for handling IBL data. The authors present an eigenimage-based method for compressing IBL data. The method exploits the correlation among reference images. Since there is a huge number of images and pixel values, the cascade recursive least square (CRLS) network based PCA is used to extract eigenimages. Afterwards, the wavelet approach is used for compressing those eigenimages. Simulation results demonstrate that this approach is much superior to that of compressing each reference image with JPEG and JPEG2000.     

Statistical texture retrieval in noise using complex wavelets

This paper investigates the use of complex wavelets for statistical texture retrieval in a noisy environment, in which the query image is contaminated by noise. To account for the presence of noise, the feature extraction step is based on parameter estimation in noise where features are extracted from the noisy query image by modeling the magnitude and phase of complex subband coefficients of the clean image, and relating the model's parameters to the noisy coefficients. In addition to using only the magnitude or phase which is in the form of the relative phase, we incorporate both magnitude and phase information to further improve the accuracy rate. The simulation results show the retrieval rate improvement by estimating the clean parameters from the noisy query image instead of assuming that the query image is clean. Furthermore, using both magnitude and phase of complex coefficients improves the accuracy rate from using either magnitude or phase alone, and that using complex-valued wavelets yields higher rate than using real-valued wavelets.     

Automotive signal diagnostics using wavelets and machine learning

In this paper, we describe an intelligent signal analysis system employing the wavelet transformation in the solution of vehicle engine diagnosis problems. Vehicle engine diagnosis often involves multiple signal analysis. The developed system first partitions a leading signal into small segments representing physical events or states based on wavelet multi-resolution analysis. Second, by applying the segmentation result of the leading signal to the other signals, the detailed properties of each segment, including inter-signal relationships, are extracted to form a feature vector. Finally, a fuzzy intelligent system is used to learn diagnostic features from a training set containing feature vectors extracted from signal segments at various vehicle states. The fuzzy system applies its diagnostic knowledge to classify signals as abnormal or normal. The implementation of the system is described and experiment results are presented     

Estimation of fractal signals using wavelets and filter banks

A filter bank design based on orthonormal wavelets and equipped with a multiscale Wiener filter was recently proposed for signal restoration and for signal smoothing of 1/f family of fractal signals corrupted by external noise. The conclusions obtained in these papers are based on the following simplificative hypotheses: (1) The wavelet transformation is a whitening filter, and (2) the approximation term of the wavelet expansion can be avoided when the number of octaves in the multiresolution analysis is large enough. In this paper, we show that the estimation of 1/f processes in noise can be improved avoiding these two hypotheses. Explicit expressions of the mean-square error are given, and numerical comparisons with previous results are shown