Estimation of the lateral correlation structure of subsurface water content from surface-based ground-penetrating radar reflection images

Over the past decade, significant interest has been expressed in relating the spatial statistics of surface-based reflection ground-penetrating radar (GPR) data to those of the imaged subsurface volume. A primary motivation for this work is that changes in the radar wave velocity, which largely control the character of the observed data, are expected to be related to corresponding changes in subsurface water content. Although previous work has indeed indicated that the spatial statistics of GPR images are linked to those of the water content distribution of the probed region, a viable method for quantitatively analyzing the GPR data and solving the corresponding inverse problem has not yet been presented. Here we address this issue by first deriving a relationship between the 2-D autocorrelation of a water content distribution and that of the corresponding GPR reflection image. We then show how a Bayesian inversion strategy based on Markov chain Monte Carlo sampling can be used to estimate the posterior distribution of subsurface correlation model parameters that are consistent with the GPR data. Our results indicate that if the underlying assumptions are valid and we possess adequate prior knowledge regarding the water content distribution, in particular its vertical variability, this methodology allows not only for the reliable recovery of lateral correlation model parameters but also for estimates of parameter uncertainties. In the case where prior knowledge regarding the vertical variability of water content is not available, the results show that the methodology still reliably recovers the aspect ratio of the heterogeneity.

Priors about observables in vector autoregressions

Standard practice in Bayesian VARs is to formulate priors on the autoregressive parameters, but economists and policy makers actually have priors about the behavior of observable variables. We show how this kind of prior can be used in a VAR under strict probability theory principles. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations with a very large number of parameters. We prove various convergence theorems for the algorithm. As an application, we first show that the results in Christiano et al. (1999) are very sensitive to the introduction of various priors that are widely used. These priors turn out to be associated with undesirable priors on observables. But an empirical prior on observables helps clarify the relevance of these estimates: we find much higher persistence of output responses to monetary policy shocks than the one reported in Christiano et al. (1999) and a significantly larger total effect.

Source wavelet estimation for waveform inversion of crosshole georadar data

AbstractFor a wide range of environmental, hydrological, and engineering applications there is a fast growing need for high-resolution imaging. In this context, waveform tomographic imaging of crosshole georadar data is a powerful method able to provide images of pertinent electrical properties in near-surface environments with unprecedented spatial resolution. In contrast, conventional ray-based tomographic methods, which consider only a very limited part of the recorded signal (first-arrival traveltimes and maximum first-cycle amplitudes), suffer from inherent limitations in resolution and may prove to be inadequate in complex environments. For a typical crosshole georadar survey the potential improvement in resolution when using waveform-based approaches instead of ray-based approaches is in the range of one order-of- magnitude. Moreover, the spatial resolution of waveform-based inversions is comparable to that of common logging methods. While in exploration seismology waveform tomographic imaging has become well established over the past two decades, it is comparably still underdeveloped in the georadar domain despite corresponding needs. Recently, different groups have presented finite-difference time-domain waveform inversion schemes for crosshole georadar data, which are adaptations and extensions of Tarantola's seminal nonlinear generalized least-squares approach developed for the seismic case. First applications of these new crosshole georadar waveform inversion schemes on synthetic and field data have shown promising results. However, there is little known about the limits and performance of such schemes in complex environments. To this end, the general motivation of my thesis is the evaluation of the robustness and limitations of waveform inversion algorithms for crosshole georadar data in order to apply such schemes to a wide range of real world problems.One crucial issue to making applicable and effective any waveform scheme to real-world crosshole georadar problems is the accurate estimation of the source wavelet, which is unknown in reality. Waveform inversion schemes for crosshole georadar data require forward simulations of the wavefield in order to iteratively solve the inverse problem. Therefore, accurate knowledge of the source wavelet is critically important for successful application of such schemes. Relatively small differences in the estimated source wavelet shape can lead to large differences in the resulting tomograms. In the first part of my thesis, I explore the viability and robustness of a relatively simple iterative deconvolution technique that incorporates the estimation of the source wavelet into the waveform inversion procedure rather than adding additional model parameters into the inversion problem. Extensive tests indicate that this source wavelet estimation technique is simple yet effective, and is able to provide remarkably accurate and robust estimates of the source wavelet in the presence of strong heterogeneity in both the dielectric permittivity and electrical conductivity as well as significant ambient noise in the recorded data. Furthermore, our tests also indicate that the approach is insensitive to the phase characteristics of the starting wavelet, which is not the case when directly incorporating the wavelet estimation into the inverse problem.Another critical issue with crosshole georadar waveform inversion schemes which clearly needs to be investigated is the consequence of the common assumption of frequency- independent electromagnetic constitutive parameters. This is crucial since in reality, these parameters are known to be frequency-dependent and complex and thus recorded georadar data may show significant dispersive behaviour. In particular, in the presence of water, there is a wide body of evidence showing that the dielectric permittivity can be significantly frequency dependent over the GPR frequency range, due to a variety of relaxation processes. The second part of my thesis is therefore dedicated to the evaluation of the reconstruction limits of a non-dispersive crosshole georadar waveform inversion scheme in the presence of varying degrees of dielectric dispersion. I show that the inversion algorithm, combined with the iterative deconvolution-based source wavelet estimation procedure that is partially able to account for the frequency-dependent effects through an "effective" wavelet, performs remarkably well in weakly to moderately dispersive environments and has the ability to provide adequate tomographic reconstructions.

Estimation of the correlation structure of crustal velocity heterogeneity from seismic reflection data

Numerous sources of evidence point to the fact that heterogeneity within the Earth's deep crystalline crust is complex and hence may be best described through stochastic rather than deterministic approaches. As seismic reflection imaging arguably offers the best means of sampling deep crustal rocks in situ, much interest has been expressed in using such data to characterize the stochastic nature of crustal heterogeneity. Previous work on this problem has shown that the spatial statistics of seismic reflection data are indeed related to those of the underlying heterogeneous seismic velocity distribution. As of yet, however, the nature of this relationship has remained elusive due to the fact that most of the work was either strictly empirical or based on incorrect methodological approaches. Here, we introduce a conceptual model, based on the assumption of weak scattering, that allows us to quantitatively link the second-order statistics of a 2-D seismic velocity distribution with those of the corresponding processed and depth-migrated seismic reflection image. We then perform a sensitivity study in order to investigate what information regarding the stochastic model parameters describing crustal velocity heterogeneity might potentially be recovered from the statistics of a seismic reflection image using this model. Finally, we present a Monte Carlo inversion strategy to estimate these parameters and we show examples of its application at two different source frequencies and using two different sets of prior information. Our results indicate that the inverse problem is inherently non-unique and that many different combinations of the vertical and lateral correlation lengths describing the velocity heterogeneity can yield seismic images with the same 2-D autocorrelation structure. The ratio of all of these possible combinations of vertical and lateral correlation lengths, however, remains roughly constant which indicates that, without additional prior information, the aspect ratio is the only parameter describing the stochastic seismic velocity structure that can be reliably recovered.

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.

Reconstruction of the join time-delay Doppler-scale reflectivity density in the wide-band regime. A frame theory based approach

The inverse scattering problem concerning the determination of the joint time-delayDoppler-scale reflectivity density characterizing continuous target environments is addressed by recourse to the generalized frame theory. A reconstruction formula,involving the echoes of a frame of outgoing signals and its corresponding reciprocalframe, is developed. A ‘‘realistic’’ situation with respect to the transmission ofa finite number of signals is further considered. In such a case, our reconstruction formula is shown to yield the orthogonal projection of the reflectivity density onto a subspace generated by the transmitted signals.

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

Uso de redes neurais recorrentes na determinação das constantes de acidez para a 7-epiclusianona em misturas etanol-água

This work propose a recursive neural network to solve inverse equilibrium problem. The acidity constants of 7-epiclusianone in ethanol-water binary mixtures were determined from multiwavelength spectrophotmetric data. A linear relationship between acidity constants and the %w/v of ethanol in the solvent mixture was observed. The proposed method efficiency is compared with the Simplex method, commonly used in nonlinear optimization techniques. The neural network method is simple, numerically stable and has a broad range of applicability.

Proposal of methodology for the modeling and control of manipulators

Industrial applications demand that robots operate in agreement with the position and orientation of their end effector. It is necessary to solve the kinematics inverse problem. This allows the displacement of the joints of the manipulator to be determined, to accomplish a given objective. Complete studies of dynamical control of joint robotics are also necessary. Initially, this article focuses on the implementation of numerical algorithms for the solution of the kinematics inverse problem and the modeling and simulation of dynamic systems. This is done using real time implementation. The modeling and simulation of dynamic systems are performed emphasizing off-line programming. In sequence, a complete study of the control strategies is carried out through the study of several elements of a robotic joint, such as: DC motor, inertia, and gearbox. Finally a trajectory generator, used as input for a generic group of joints, is developed and a proposal of the controller's implementation of joints, using EPLD development system, is presented.

Aircraft tracking and classification with vhf passive bistatic radar

Since the times preceding the Second World War the subject of aircraft tracking has been a core interest to both military and non-military aviation. During subsequent years both technology and configuration of the radars allowed the users to deploy it in numerous fields, such as over-the-horizon radar, ballistic missile early warning systems or forward scatter fences. The latter one was arranged in a bistatic configuration. The bistatic radar has continuously re-emerged over the last eighty years for its intriguing capabilities and challenging configuration and formulation. The bistatic radar arrangement is used as the basis of all the analyzes presented in this work. The aircraft tracking method of VHF Doppler-only information, developed in the first part of this study, is solely based on Doppler frequency readings in relation to time instances of their appearance. The corresponding inverse problem is solved by utilising a multistatic radar scenario with two receivers and one transmitter and using their frequency readings as a base for aircraft trajectory estimation. The quality of the resulting trajectory is then compared with ground-truth information based on ADS-B data. The second part of the study deals with the developement of a method for instantaneous Doppler curve extraction from within a VHF time-frequency representation of the transmitted signal, with a three receivers and one transmitter configuration, based on a priori knowledge of the probability density function of the first order derivative of the Doppler shift, and on a system of blocks for identifying, classifying and predicting the Doppler signal. The extraction capabilities of this set-up are tested with a recorded TV signal and simulated synthetic spectrograms. Further analyzes are devoted to more comprehensive testing of the capabilities of the extraction method. Besides testing the method, the classification of aircraft is performed on the extracted Bistatic Radar Cross Section profiles and the correlation between them for different types of aircraft. In order to properly estimate the profiles, the ADS-B aircraft location information is adjusted based on extracted Doppler frequency and then used for Bistatic Radar Cross Section estimation. The classification is based on seven types of aircraft grouped by their size into three classes.

Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels

This work investigates theoretical properties of symmetric and anti-symmetric kernels. First chapters give an overview of the theory of kernels used in supervised machine learning. Central focus is on the regularized least squares algorithm, which is motivated as a problem of function reconstruction through an abstract inverse problem. Brief review of reproducing kernel Hilbert spaces shows how kernels define an implicit hypothesis space with multiple equivalent characterizations and how this space may be modified by incorporating prior knowledge. Mathematical results of the abstract inverse problem, in particular spectral properties, pseudoinverse and regularization are recollected and then specialized to kernels. Symmetric and anti-symmetric kernels are applied in relation learning problems which incorporate prior knowledge that the relation is symmetric or anti-symmetric, respectively. Theoretical properties of these kernels are proved in a draft this thesis is based on and comprehensively referenced here. These proofs show that these kernels can be guaranteed to learn only symmetric or anti-symmetric relations, and they can learn any relations relative to the original kernel modified to learn only symmetric or anti-symmetric parts. Further results prove spectral properties of these kernels, central result being a simple inequality for the the trace of the estimator, also called the effective dimension. This quantity is used in learning bounds to guarantee smaller variance.

Extracting Ramachandran torsional angle distributions from 2D NMR data using Tikhonov regularization /

Solid state nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for studying structural and dynamical properties of disordered and partially ordered materials, such as glasses, polymers, liquid crystals, and biological materials. In particular, twodimensional( 2D) NMR methods such as ^^C-^^C correlation spectroscopy under the magicangle- spinning (MAS) conditions have been used to measure structural constraints on the secondary structure of proteins and polypeptides. Amyloid fibrils implicated in a broad class of diseases such as Alzheimer's are known to contain a particular repeating structural motif, called a /5-sheet. However, the details of such structures are poorly understood, primarily because the structural constraints extracted from the 2D NMR data in the form of the so-called Ramachandran (backbone torsion) angle distributions, g{^,'4)), are strongly model-dependent. Inverse theory methods are used to extract Ramachandran angle distributions from a set of 2D MAS and constant-time double-quantum-filtered dipolar recoupling (CTDQFD) data. This is a vastly underdetermined problem, and the stability of the inverse mapping is problematic. Tikhonov regularization is a well-known method of improving the stability of the inverse; in this work it is extended to use a new regularization functional based on the Laplacian rather than on the norm of the function itself. In this way, one makes use of the inherently two-dimensional nature of the underlying Ramachandran maps. In addition, a modification of the existing numerical procedure is performed, as appropriate for an underdetermined inverse problem. Stability of the algorithm with respect to the signal-to-noise (S/N) ratio is examined using a simulated data set. The results show excellent convergence to the true angle distribution function g{(j),ii) for the S/N ratio above 100.

Modélisation de la diffraction des ondes de cisaillement en élastographie dynamique ultrasonore

L'élastographie ultrasonore est une technique d'imagerie émergente destinée à cartographier les paramètres mécaniques des tissus biologiques, permettant ainsi d’obtenir des informations diagnostiques additionnelles pertinentes. La méthode peut ainsi être perçue comme une extension quantitative et objective de l'examen palpatoire. Diverses techniques élastographiques ont ainsi été proposées pour l'étude d'organes tels que le foie, le sein et la prostate et. L'ensemble des méthodes proposées ont en commun une succession de trois étapes bien définies: l'excitation mécanique (statique ou dynamique) de l'organe, la mesure des déplacements induits (réponse au stimulus), puis enfin, l'étape dite d'inversion, qui permet la quantification des paramètres mécaniques, via un modèle théorique préétabli. Parallèlement à la diversification des champs d'applications accessibles à l'élastographie, de nombreux efforts sont faits afin d'améliorer la précision ainsi que la robustesse des méthodes dites d'inversion. Cette thèse regroupe un ensemble de travaux théoriques et expérimentaux destinés à la validation de nouvelles méthodes d'inversion dédiées à l'étude de milieux mécaniquement inhomogènes. Ainsi, dans le contexte du diagnostic du cancer du sein, une tumeur peut être perçue comme une hétérogénéité mécanique confinée, ou inclusion, affectant la propagation d'ondes de cisaillement (stimulus dynamique). Le premier objectif de cette thèse consiste à formuler un modèle théorique capable de prédire l'interaction des ondes de cisaillement induites avec une tumeur, dont la géométrie est modélisée par une ellipse. Après validation du modèle proposé, un problème inverse est formulé permettant la quantification des paramètres viscoélastiques de l'inclusion elliptique. Dans la continuité de cet objectif, l'approche a été étendue au cas d'une hétérogénéité mécanique tridimensionnelle et sphérique avec, comme objectifs additionnels, l'applicabilité aux mesures ultrasonores par force de radiation, mais aussi à l'estimation du comportement rhéologique de l'inclusion (i.e., la variation des paramètres mécaniques avec la fréquence d'excitation). Enfin, dans le cadre de l'étude des propriétés mécaniques du sang lors de la coagulation, une approche spécifique découlant de précédents travaux réalisés au sein de notre laboratoire est proposée. Celle-ci consiste à estimer la viscoélasticité du caillot sanguin via le phénomène de résonance mécanique, ici induit par force de radiation ultrasonore. La méthode, dénommée ARFIRE (''Acoustic Radiation Force Induced Resonance Elastography'') est appliquée à l'étude de la coagulation de sang humain complet chez des sujets sains et sa reproductibilité est évaluée.

Développement d'une nouvelle méthode de caractérisation tissulaire basée sur l'élastographie ultrasonore : application pour le dépistage précoce du cancer du sein

Le cancer du sein est le cancer le plus fréquent chez la femme. Il demeure la cause de mortalité la plus importante chez les femmes âgées entre 35 et 55 ans. Au Canada, plus de 20 000 nouveaux cas sont diagnostiqués chaque année. Les études scientifiques démontrent que l'espérance de vie est étroitement liée à la précocité du diagnostic. Les moyens de diagnostic actuels comme la mammographie, l'échographie et la biopsie comportent certaines limitations. Par exemple, la mammographie permet de diagnostiquer la présence d’une masse suspecte dans le sein, mais ne peut en déterminer la nature (bénigne ou maligne). Les techniques d’imagerie complémentaires comme l'échographie ou l'imagerie par résonance magnétique (IRM) sont alors utilisées en complément, mais elles sont limitées quant à la sensibilité et la spécificité de leur diagnostic, principalement chez les jeunes femmes (< 50 ans) ou celles ayant un parenchyme dense. Par conséquent, nombreuses sont celles qui doivent subir une biopsie alors que leur lésions sont bénignes. Quelques voies de recherche sont privilégiées depuis peu pour réduire l`incertitude du diagnostic par imagerie ultrasonore. Dans ce contexte, l’élastographie dynamique est prometteuse. Cette technique est inspirée du geste médical de palpation et est basée sur la détermination de la rigidité des tissus, sachant que les lésions en général sont plus rigides que le tissu sain environnant. Le principe de cette technique est de générer des ondes de cisaillement et d'en étudier la propagation de ces ondes afin de remonter aux propriétés mécaniques du milieu via un problème inverse préétabli. Cette thèse vise le développement d'une nouvelle méthode d'élastographie dynamique pour le dépistage précoce des lésions mammaires. L'un des principaux problèmes des techniques d'élastographie dynamiques en utilisant la force de radiation est la forte atténuation des ondes de cisaillement. Après quelques longueurs d'onde de propagation, les amplitudes de déplacement diminuent considérablement et leur suivi devient difficile voir impossible. Ce problème affecte grandement la caractérisation des tissus biologiques. En outre, ces techniques ne donnent que l'information sur l'élasticité tandis que des études récentes montrent que certaines lésions bénignes ont les mêmes élasticités que des lésions malignes ce qui affecte la spécificité de ces techniques et motive la quantification de d'autres paramètres mécaniques (e.g.la viscosité). Le premier objectif de cette thèse consiste à optimiser la pression de radiation acoustique afin de rehausser l'amplitude des déplacements générés. Pour ce faire, un modèle analytique de prédiction de la fréquence de génération de la force de radiation a été développé. Une fois validé in vitro, ce modèle a servi pour la prédiction des fréquences optimales pour la génération de la force de radiation dans d'autres expérimentations in vitro et ex vivo sur des échantillons de tissu mammaire obtenus après mastectomie totale. Dans la continuité de ces travaux, un prototype de sonde ultrasonore conçu pour la génération d'un type spécifique d'ondes de cisaillement appelé ''onde de torsion'' a été développé. Le but est d'utiliser la force de radiation optimisée afin de générer des ondes de cisaillement adaptatives, et de monter leur utilité dans l'amélioration de l'amplitude des déplacements. Contrairement aux techniques élastographiques classiques, ce prototype permet la génération des ondes de cisaillement selon des parcours adaptatifs (e.g. circulaire, elliptique,…etc.) dépendamment de la forme de la lésion. L’optimisation des dépôts énergétiques induit une meilleure réponse mécanique du tissu et améliore le rapport signal sur bruit pour une meilleure quantification des paramètres viscoélastiques. Il est aussi question de consolider davantage les travaux de recherches antérieurs par un appui expérimental, et de prouver que ce type particulier d'onde de torsion peut mettre en résonance des structures. Ce phénomène de résonance des structures permet de rehausser davantage le contraste de déplacement entre les masses suspectes et le milieu environnant pour une meilleure détection. Enfin, dans le cadre de la quantification des paramètres viscoélastiques des tissus, la dernière étape consiste à développer un modèle inverse basé sur la propagation des ondes de cisaillement adaptatives pour l'estimation des paramètres viscoélastiques. L'estimation des paramètres viscoélastiques se fait via la résolution d'un problème inverse intégré dans un modèle numérique éléments finis. La robustesse de ce modèle a été étudiée afin de déterminer ces limites d'utilisation. Les résultats obtenus par ce modèle sont comparés à d'autres résultats (mêmes échantillons) obtenus par des méthodes de référence (e.g. Rheospectris) afin d'estimer la précision de la méthode développée. La quantification des paramètres mécaniques des lésions permet d'améliorer la sensibilité et la spécificité du diagnostic. La caractérisation tissulaire permet aussi une meilleure identification du type de lésion (malin ou bénin) ainsi que son évolution. Cette technique aide grandement les cliniciens dans le choix et la planification d'une prise en charge adaptée.

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images