Discrete Wavelet Transform and Optimal Spectral Transform Applied to Multicomponent Image Coding

ISBN 978-953-307-482-5 Chapitre 10International audienc

A nonlinear Stein based estimator for multichannel image denoising

The use of multicomponent images has become widespread with the improvement of multisensor systems having increased spatial and spectral resolutions. However, the observed images are often corrupted by an additive Gaussian noise. In this paper, we are interested in multichannel image denoising based on a multiscale representation of the images. A multivariate statistical approach is adopted to take into account both the spatial and the inter-component correlations existing between the different wavelet subbands. More precisely, we propose a new parametric nonlinear estimator which generalizes many reported denoising methods. The derivation of the optimal parameters is achieved by applying Stein's principle in the multivariate case. Experiments performed on multispectral remote sensing images clearly indicate that our method outperforms conventional wavelet denoising technique

ICA-Based Algorithm Applied to Image Coding

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.International audienceRecently, Narozny et al [1] proposed a new viewpoint in variable high-rate transform coding. They showed that the problem of finding the optimal 1-D linear block transform for a coding system employing entropy-constrained uniform quantization may be viewed as a modified independent component analysis (ICA) problem. By adopting this new viewpoint, two new ICA-based algorithms, called GCGsup and ICAorth, were then derived for computing respectively the optimal linear transform and the optimal orthogonal transform. In this paper, we show that the transforms returned by GCGsup and ICAorth can achieve better visual image quality(better preservation of lines and contours) than the KLT and 2-D Discrete Cosine Transform (DCT) when applied to the compression of well-known grayscale images

Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Multirate Frequency Transformations: Wideband AM-FM Demodulation with Applications to Signal Processing and Communications

The AM-FM (amplitude & frequency modulation) signal model finds numerous applications in image processing, communications, and speech processing. The traditional approaches towards demodulation of signals in this category are the analytic signal approach, frequency tracking, or the energy operator approach. These approaches however, assume that the amplitude and frequency components are slowly time-varying, e.g., narrowband and incur significant demodulation error in the wideband scenarios. In this thesis, we extend a two-stage approach towards wideband AM-FM demodulation that combines multirate frequency transformations (MFT) enacted through a combination of multirate systems with traditional demodulation techniques, e.g., the Teager-Kasiser energy operator demodulation (ESA) approach to large wideband to narrowband conversion factors. The MFT module comprises of multirate interpolation and heterodyning and converts the wideband AM-FM signal into a narrowband signal, while the demodulation module such as ESA demodulates the narrowband signal into constituent amplitude and frequency components that are then transformed back to yield estimates for the wideband signal. This MFT-ESA approach is then applied to the various problems of: (a) wideband image demodulation and fingerprint demodulation, where multidimensional energy separation is employed, (b) wideband first-formant demodulation in vowels, and (c) wideband CPM demodulation with partial response signaling, to demonstrate its validity in both monocomponent and multicomponent scenarios as an effective multicomponent AM-FM signal demodulation and analysis technique for image processing, speech processing, and communications based applications

Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images

The high resolution of the hyperspectral remote sensing images available allows the detailed analysis of even small spatial structures. As a consequence, the study of techniques to efficiently extract spatial information is a very active realm. In this paper, we propose a novel denoising wavelet-based profile for the extraction of spatial information that does not require parameters fixed by the user. Over each band obtained by a wavelet-based feature extraction technique, a denoising profile (DP) is built through the recursive application of discrete wavelet transforms followed by a thresholding process. Each component of the DP consists of features reconstructed by recursively applying inverse wavelet transforms to the thresholded coefficients. Several thresholding methods are explored. In order to show the effectiveness of the extended DP (EDP), we propose a classification scheme based on the computation of the EDP and supervised classification by extreme learning machine. The obtained results are compared to other state-of-the-art methods based on profiles in the literature. An additional study of behavior in the presence of added noise is also performed showing the high reliability of the EDP proposedThis work was supported in part by the Consellería de Educación, Universidade e Formación Profesional under Grants GRC2014/008 and ED431C 2018/2019 and the Ministerio de Economía y Empresa, Gobierno de España under Grant TIN2016-76373-P. Both are cofunded by the European Regional Development FundS

Characterization of the structure, stability, mechanical and electrochemical properties of metallic glasses

Metallic glasses are often referred as glassy or amorphous alloys. They lack long-range order and microstructural defects that are characteristics in crystals, such as grain and phase boundaries and dislocations. These new materials have demonstrated very interesting structural and mechanical properties derived from their homogeneity in composition and the absence of grain boundaries. Structural, mechanical or chemical properties, among others, may be even superior to those observed in conventional metallic alloys, and therefore attracted great scientific and technological interest. In this thesis project three different families of metallic glasses were selected to achieve a better understanding of amorphous alloys. First, a Ce-based alloy has been used to analyze a polyamorphic transition upon application of pressure to a more densely packed structure. X-ray diffraction and inelastic x-ray scattering data show a polyamorphic transition in the 2-10 GPa range, and this transition presents a hysteresis cycle between both compression and decompression data. The effect of this transition on mechanical properties is then evaluated. Second, a family of Fe-based metallic glasses, or amorphous steels, was selected to study their mechanical and electrochemical properties as a function of the structure and composition. The composition of the base alloy was first modified by addition of Yttrium in different concentrations as microalloying element and the structure was changed by thermal annealing, forming intermediate crystal/amorphous composites, up to a complete crystallization state. Finally, an entirely new alloy for biocompatible purposes has been designed, synthesized, and characterized. The basic structural characterization of this new Zr-Ti based amorphous alloy shows that is possible to produce the amorphous state in an alloy that does not contain toxic or unhealthy elements.Los vidrios metálicos, comúnmente conocidos como aleaciones amorfas o vitrificadas, carecen de orden a largo alcance así como de defectos microestructurales comunes en los cristales, tales como dislocaciones o fronteras de grano y/o de fase. Esta nueva clase de materiales ha demostrado poseer propiedades muy interesantes derivadas de la ausencia de microestructura y la homogeneidad en su composición. Estas propiedades, estructurales, mecánicas y químicas, entre otras, pueden llegar a ser incluso superiores a las observadas en materiales convencionales, y por lo tanto los vidrios metálicos han atraído gran interés por parte de la comunidad científica así como de carácter tecnológico. En este proyecto de tesis se pretende obtener un mayor conocimiento sobre aleaciones metálicas amorfas, para lo cual se propusieron tres familias diferentes de vidrios metálicos. Primero, se utilizó una aleación de base Ce para analizar transiciones poliamórficas, entre un estado de baja densidad hacia una estructura densamente empaquetada, por efecto de la presión. Los resultados obtenidos por difracción de rayos X y dispersión inelástica de rayos X muestran una transición en un rango de presiones de 2 a 10 GPa presentando además histéresis con respecto a los datos obtenidos en compresión y descompresión. El efecto de dicha transición en las propiedades mecánicas de la aleación es también evaluado. En segundo lugar se eligió una familia de aleaciones de base Fe, conocidos también como aceros amorfos. Las propiedades mecánicas y electroquímicas en función de la estructura y la composición fueron evaluadas mediante la introducción de itrio como elemento microaleante y la modificación de la estructura por medio de tratamientos térmicos con la obtención de estructuras compuestas nanocristal-amorfo hasta una completa cristalización. Finalmente, se diseñó y sintetizó una aleación completamente nueva con el propósito de evaluar biocompatibilidad. La caracterización estructural básica de esta nueva aleación de base Zr-Ti sin elementos tóxicos y/o alergénicos muestra que es posible obtener aleaciones amorfas con las composiciones propuesta