Contourlet Domain Image Modeling and its Applications in Watermarking and Denoising

Statistical image modeling in sparse domain has recently attracted a great deal of research interest. Contourlet transform as a two-dimensional transform with multiscale and multi-directional properties is known to effectively capture the smooth contours and geometrical structures in images. The objective of this thesis is to study the statistical properties of the contourlet coefficients of images and develop statistically-based image denoising and watermarking schemes. Through an experimental investigation, it is first established that the distributions of the contourlet subband coefficients of natural images are significantly non-Gaussian with heavy-tails and they can be best described by the heavy-tailed statistical distributions, such as the alpha-stable family of distributions. It is shown that the univariate members of this family are capable of accurately fitting the marginal distributions of the empirical data and that the bivariate members can accurately characterize the inter-scale dependencies of the contourlet coefficients of an image. Based on the modeling results, a new method in image denoising in the contourlet domain is proposed. The Bayesian maximum a posteriori and minimum mean absolute error estimators are developed to determine the noise-free contourlet coefficients of grayscale and color images. Extensive experiments are conducted using a wide variety of images from a number of databases to evaluate the performance of the proposed image denoising scheme and to compare it with that of other existing schemes. It is shown that the proposed denoising scheme based on the alpha-stable distributions outperforms these other methods in terms of the peak signal-to-noise ratio and mean structural similarity index, as well as in terms of visual quality of the denoised images. The alpha-stable model is also used in developing new multiplicative watermark schemes for grayscale and color images. Closed-form expressions are derived for the log-likelihood-based multiplicative watermark detection algorithm for grayscale images using the univariate and bivariate Cauchy members of the alpha-stable family. A multiplicative multichannel watermark detector is also designed for color images using the multivariate Cauchy distribution. Simulation results demonstrate not only the effectiveness of the proposed image watermarking schemes in terms of the invisibility of the watermark, but also the superiority of the watermark detectors in providing detection rates higher than that of the state-of-the-art schemes even for the watermarked images undergone various kinds of attacks

Uncertainties in the Estimation of the Shear-Wave Velocity and the Small-Strain Damping Ratio from Surface Wave Analysis

L'abstract è presente nell'allegato / the abstract is in the attachmen

Composite and Cascaded Generalized-K Fading Channel Modeling and Their Diversity and Performance Analysis

The introduction of new schemes that are based on the communication among nodes has motivated the use of composite fading models due to the fact that the nodes experience different multipath fading and shadowing statistics, which subsequently determines the required statistics for the performance analysis of different transceivers. The end-to-end signal-to-noise-ratio (SNR) statistics plays an essential role in the determination of the performance of cascaded digital communication systems. In this thesis, a closed-form expression for the probability density function (PDF) of the end-end SNR for independent but not necessarily identically distributed (i.n.i.d.) cascaded generalized-K (GK) composite fading channels is derived. The developed PDF expression in terms of the Meijer-G function allows the derivation of subsequent performance metrics, applicable to different modulation schemes, including outage probability, bit error rate for coherent as well as non-coherent systems, and average channel capacity that provides insights into the performance of a digital communication system operating in N cascaded GK composite fading environment. Another line of research that was motivated by the introduction of composite fading channels is the error performance. Error performance is one of the main performance measures and derivation of its closed-form expression has proved to be quite involved for certain systems. Hence, in this thesis, a unified closed-form expression, applicable to different binary modulation schemes, for the bit error rate of dual-branch selection diversity based systems undergoing i.n.i.d. GK fading is derived in terms of the extended generalized bivariate Meijer G-function

Sub-diffraction limited morphology characterization in single noble metal nanoparticles and single conjugated polymer chains using optical microscopy techniques

At the nanoscale, materials exhibit special properties not present in the bulk, which may be exploited in diverse applications that include catalysis, sensing, and energy harvest and transfer. Due to their small size, nanoscale materials also present a characterization challenge, because optical microscopy techniques cannot resolve images of structural features smaller than finite lenses may focus visible light. Optical images of nanoparticles or single molecules show diffraction-limited spots with radii of approximately half the wavelength of the light used to interrogate them, and the underlying structure of the nanoscale object is not obvious to the eye. Fortunately, manipulation of excitation conditions and image processing techniques can tease out information about the morphology of nanomaterials investigated. The first example presented in this dissertation shows how an asymmetric excitation geometry and polarization spectroscopy elucidate the orientation of single silver triangular nanoprisms in the plane of an optical microscope’s stage. Characterizing this orientation using optical microscopy techniques opens possibilities for post-characterization nanoparticle functionalization and improved amplification of surface-enhanced spectroscopy signals. Electron microscopy may characterize single noble metal nanoparticles if one is unconcerned with those benefits, but electron microscopy investigations are more challenging for soft matter samples, so optical characterization becomes even more appealing for polymer studies. Bias-induced centroid (BIC) spectroscopy, correlated with polarization spectroscopy, reports not only on the distance over which highly ordered single poly[2-methoxy-5-(2’-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV) chains transfer energy, but also that the rod-like structures these polymers are believed to adopt are likely to transfer this energy along their longitudinal axes. BIC relies on observable changes in the position of the fluorescence centroid, but when the bias-induced hole-injection partially quenching the fluorescence occurs symmetrically, the displacement of the fluorescence centroid is small, and defining the displacement direction becomes difficult. In this event, analysis of the ellipticity of the diffraction-limited images of the MEH-PPV fluorescence also supports the conclusion that the polymer transfers energy in the direction of the longitudinal axis of the rod-like structure. Taken together, these wide-field optical techniques allow simultaneous morphological characterization of many single nanoparticles or single polymer chains without appealing to scanning probe or electron microscopies, which can damage the sample or prevent post-characterization modification.Chemistr