Doctor of Philosophy

dissertationThe gold standard for evaluation of arterial disease using MR continues to be contrast-enhanced MR angiography (MRA) with gadolinium-based contrast agents (Gd-MRA). There has been a recent resurgence in interest in methods that do not rely on gadolinium for enhancement of blood vessels due to associations Gd-MRA has with nephrogenic systemic fibrosis (NSF) in patients with impaired renal function. The risk due to NSF has been shown to be minimized when selecting the appropriate contrast type and dose. Even though the risk of NSF has been shown to be minimized, demand for noncontrast MRA has continued to rise to reduce examination cost, and improve patient comfort and ability to repeat scans. Several methods have been proposed and used to perform angiography of the aorta and peripheral arteries without the use of gadolinium. These techniques have had limitations in transmit radiofrequency field (B1+) inhomogeneities, acquisition time, and specific hardware requirements, which have stunted the utility of noncontrast enhanced MRA. In this work feasibility of noncontrast (NC) MRA at 3T of the femoral arteries using dielectric padding, and using 3D radial stack of stars and compressed sensing to accelerate acquisitions in the abdomen and thorax were tested. Imaging was performed on 13 subjects in the pelvis and thighs using high permittivity padding, and 11 in the abdomen and 19 in the thorax using 3D radial stack of stars with tiny golden angle using gold standards or previously published techniques. Qualitative scores for each study were determined by radiologists who were blinded to acquisition type. Vessel conspicuity in the thigh and pelvis showed significant increase when high permittivity padding was used in the acquisition. No significant difference in image quality was observed in the abdomen and thorax when using undersampling, except for the descending aorta in thoracic imaging. All image quality scores were determined to be of diagnostic quality. In this work it is shown that NC-MRA can be improved through the use of high permittivity dielectric padding and acquisition time can be decreased through the use of 3D radial stack of stars acquisitions

Multi-modal dictionary learning for image separation with application in art investigation

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component models features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data - taken from digital acquisition of the Ghent Altarpiece (1432) - confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.Comment: submitted to IEEE Transactions on Images Processin

Image-Based Modeling of Porous Media Using FEM and Lagrangian Particle Tracking

The study of fundamental flow and transport processes at the pore scale is essential to understanding how the mechanisms affect larger, field-scale, processes that occur in oil and gas recovery, groundwater flow, contaminant transport, and CO2 sequestration. Pore-scale imaging and modeling is one of the techniques used to investigate these fundamental mechanisms. Although extensive development of pore-scale imaging and modeling has occurred recently, some areas still need further advances. In this work, we address two areas: (1) imaging of bulk proppants and proppant-filled fractures under varying loading stress and flow simulation in these systems and (2) nanoparticle (NP) transport modeling in porous media. These are briefly explained below. Rock fracturing, followed by proppant injection, has been used for years to improve oil and gas production rates in low permeability reservoirs and is now routinely used in low-permeability resources such as a shales and tight sands. While field data makes clear the effectiveness of this technique, there is still much room to improve on the science, including how the proppant-filled fracture system responds to changes in loading stress which affect permeability and conductivity. Here, we use high-resolution x-ray computed tomography (XCT) to image two unsaturated rock/fracture/proppant systems under a series of stress levels typical of producing reservoirs: one with shale, one with Berea sandstone. The resulting XCT images were segmented, analyzed for structural and porosity changes, and then used for image-based flow modeling of Stokes flow using both finite element (FEM) and Lattice Boltzmann methods. NPs have been widely used commercially and have the potential to be extensively used in petroleum engineering as stabilizers in enhanced oil recovery operations or as tracers or sensors to detect rock and fluid properties. %In spite of a wide range of applications, many NP transport details are still unknown. In this work, we describe a Lagrangian particle tracking algorithm to model NP transport that can be used to better understand the impact of pore-scale hydrodynamics and surface forces on NP transport. Two XCT images, a Berea sandstone and a 2.5D micromodel, were meshed and used for image-based flow modeling of FEM Stokes flow. The effects of particle size, surface forces, flow rate, particle density, surface capacity, and surface forces mapped to XCT-image based mineralogy were studied

Advances in Micro- and Nanomechanics

This book focuses on recent advances in both theoretical and experimental studies of material behaviour at the micro- and nano-scales. Special attention is given to experimental studies of nanofilms, nanoparticles and nanocomposites as well as tooth defects. Various experimental techniques were used. Magneto- and thermoelastic coupling were considered, as were nonlocal models of thin structures

DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion

Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability. However, diffusion models have not yet received sufficient research in the field of image fusion. In this article, we introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation and designing two different conditional injection modulation modules (i.e., style transfer modulation and wavelet modulation) to inject coarse-grained style information and fine-grained high-frequency and low-frequency information into the diffusion UNet, thereby generating fused images. In addition, we also discussed the residual learning and the selection of training objectives of the diffusion model in the image fusion task. Extensive experimental results based on quantitative and qualitative assessments compared with benchmarks demonstrates state-of-the-art results and good generalization performance in image fusion tasks. Finally, it is hoped that our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks. Code shall be released for better reproducibility

Avalanching on dunes and its effects: Size statistics, stratification, & seismic surveys

Geophysical research has long been interdisciplinary, with many phenomena on the Earth's surface involving multiple, linked processes that are best understood using a combination of techniques. This is particularly true in the case of grain flows on sand dunes, in which the sedimentary stratification with which geologists are concerned arises from the granular processes investigated by physicists and engineers, and the water permeation that interests hydrologists and soil scientists determines the seismic velocities of concern to exploration geophysicists. In this dissertation, I describe four projects conducted for the degree of Doctor of Philosophy, using a combination of laboratory experimentation, fieldwork, numerical simulation, and mathematical modelling to link avalanching on dunes to its effects on stratification, on the permeation of water, and on seismic surveys. Firstly, I describe experiments on erodible, unbounded, grain piles in a channel, slowly supplied with additional grains, and I demonstrate that the behaviour of the consequent, discrete avalanches alternates between two regimes, typified by their size statistics. Reconciling the `self-organised criticality' that several authors have predicted for such a system with the hysteretic behaviour that others have observed, the system exhibits quasi-periodic, system-spanning avalanches in one regime, while in the other avalanches pass at irregular intervals and have a power-law size distribution. Secondly, I link this power-law size distribution to the strata emplaced by avalanches on bounded grain piles. A low inflow rate of grains into an experimental channel develops a pile, composed of strata in which blue-dyed, coarser grains overlie finer grains. Associating stopped avalanche fronts with the `trapped kinks' described by previous authors, I show that, in sufficiently large grain piles, mean stratum width increases linearly with distance downslope. This implies the possibility of interpreting paleodune height from the strata of aeolian sandstones, and makes predictions for the structure of avalanche-associated strata within active dunes. Thirdly, I discuss investigations of these strata within active, Qatari barchan dunes, using dye-infiltration to image strata in the field and extracting samples across individual strata with sub-centimetre resolution. Downslope increases in mean stratum width are evident, while measurements of particle size distributions demonstrate preferential permeation of water along substrata composed of finer particles, explaining the strata-associated, localised regions of high water content discovered by other work on the same dunes. Finally, I consider the effect of these within-dune variations in water content on seismic surveys for oil and gas. Having used high performance computing to simulate elastic wave propagation in the vicinity of an isolated, barchan sand dune, I demonstrate that such a dune acts as a resonator, absorbing energy from Rayleigh waves and reemitting it over an extensive period of time. I derive and validate a mathematical framework that uses bulk properties of the dune to predict quantitative properties of the emitted waves, and I demonstrate the importance of internal variations in seismic velocity, resulting from variations in water content.This work was supported by a PhD studentship within the Cambridge Earth Systems Science Doctoral Training Partnership (ESS DTP), funded by the National Environmental Research Council, grant number NE/L002507/1. Additional support was provided by Schlumberger Cambridge Research (SCR), through a CASE studentship

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Adaptive Representations for Image Restoration

In the �eld of image processing, building good representation models for natural images is crucial for various applications, such as image restora- tion, sampling, segmentation, etc. Adaptive image representation models are designed for describing the intrinsic structures of natural images. In the classical Bayesian inference, this representation is often known as the prior of the intensity distribution of the input image. Early image priors have forms such as total variation norm, Markov Random Fields (MRF), and wavelets. Recently, image priors obtained from machine learning tech- niques tend to be more adaptive, which aims at capturing the natural image models via learning from larger databases. In this thesis, we study adaptive representations of natural images for image restoration. The purpose of image restoration is to remove the artifacts which degrade an image. The degradation comes in many forms such as image blurs, noises, and artifacts from the codec. Take image denoising for an example. There are several classic representation methods which can generate state- of-the-art results. The �rst one is the assumption of image self-similarity. However, this representation has the issue that sometimes the self-similarity assumption would fail because of high noise levels or unique image contents. The second one is the wavelet based nonlocal representation, which also has a problem in that the �xed basis function is not adaptive enough for any arbitrary type of input images. The third is the sparse coding using over- complete dictionaries, which does not have the hierarchical structure that is similar to the one in human visual system and is therefore prone to denoising artifacts. My research started from image denoising. Through the thorough review and evaluation of state-of-the-art denoising methods, it was found that the representation of images is substantially important for the denoising tech- nique. At the same time, an improvement on one of the nonlocal denoising method was proposed, which improves the representation of images by the integration of Gaussian blur, clustering and Rotationally Invariant Block Matching. Enlightened by the successful application of sparse coding in compressive sensing, we exploited the image self-similarity by using a sparse representation based on wavelet coe�cients in a nonlocal and hierarchical way, which generates competitive results compared to the state-of-the-art denoising algorithms. Meanwhile, another adaptive local �lter learned by Genetic Programming (GP) was proposed for e�cient image denoising. In this work, we employed GP to �nd the optimal representations for local im- age patches through training on massive datasets, which yields competitive results compared to state-of-the-art local denoising �lters. After success- fully dealt with the denoising part, we moved to the parameter estimation for image degradation models. For instance, image blur identi�cation uses deep learning, which has recently been proposed as a popular image repre- sentation approach. This work has also been extended to blur estimation based on the fact that the second step of the framework has been replaced with general regression neural network. In a word, in this thesis, spatial cor- relations, sparse coding, genetic programming, deep learning are explored as adaptive image representation models for both image restoration and parameter estimation. We conclude this thesis by considering methods based on machine learning to be the best adaptive representations for natural images. We have shown that they can generate better results than conventional representation mod- els for the tasks of image denoising and deblurring

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches