Blind image deconvolution: nonstationary Bayesian approaches to restoring blurred photos

High quality digital images have become pervasive in modern scientific and everyday life — in areas from photography to astronomy, CCTV, microscopy, and medical imaging. However there are always limits to the quality of these images due to uncertainty and imprecision in the measurement systems. Modern signal processing methods offer the promise of overcoming some of these problems by postprocessing these blurred and noisy images. In this thesis, novel methods using nonstationary statistical models are developed for the removal of blurs from out of focus and other types of degraded photographic images. The work tackles the fundamental problem blind image deconvolution (BID); its goal is to restore a sharp image from a blurred observation when the blur itself is completely unknown. This is a “doubly illposed” problem — extreme lack of information must be countered by strong prior constraints about sensible types of solution. In this work, the hierarchical Bayesian methodology is used as a robust and versatile framework to impart the required prior knowledge. The thesis is arranged in two parts. In the first part, the BID problem is reviewed, along with techniques and models for its solution. Observation models are developed, with an emphasis on photographic restoration, concluding with a discussion of how these are reduced to the common linear spatially-invariant (LSI) convolutional model. Classical methods for the solution of illposed problems are summarised to provide a foundation for the main theoretical ideas that will be used under the Bayesian framework. This is followed by an indepth review and discussion of the various prior image and blur models appearing in the literature, and then their applications to solving the problem with both Bayesian and nonBayesian techniques. The second part covers novel restoration methods, making use of the theory presented in Part I. Firstly, two new nonstationary image models are presented. The first models local variance in the image, and the second extends this with locally adaptive noncausal autoregressive (AR) texture estimation and local mean components. These models allow for recovery of image details including edges and texture, whilst preserving smooth regions. Most existing methods do not model the boundary conditions correctly for deblurring of natural photographs, and a Chapter is devoted to exploring Bayesian solutions to this topic. Due to the complexity of the models used and the problem itself, there are many challenges which must be overcome for tractable inference. Using the new models, three different inference strategies are investigated: firstly using the Bayesian maximum marginalised a posteriori (MMAP) method with deterministic optimisation; proceeding with the stochastic methods of variational Bayesian (VB) distribution approximation, and simulation of the posterior distribution using the Gibbs sampler. Of these, we find the Gibbs sampler to be the most effective way to deal with a variety of different types of unknown blurs. Along the way, details are given of the numerical strategies developed to give accurate results and to accelerate performance. Finally, the thesis demonstrates state of the art results in blind restoration of synthetic and real degraded images, such as recovering details in out of focus photographs

Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision

Weakly-supervised vision-language (V-L) pre-training (W-VLP) aims at learning cross-modal alignment with little or no paired data, such as aligned images and captions. Recent W-VLP methods, which pair visual features with object tags, help achieve performances comparable with some VLP models trained with aligned pairs in various V-L downstream tasks. This, however, is not the case in cross-modal retrieval (XMR). We argue that the learning of such a W-VLP model is curbed and biased by the object tags of limited semantics. We address the lack of paired V-L data for model supervision with a novel Visual Vocabulary based Feature Hallucinator (WFH), which is trained via weak supervision as a W-VLP model, not requiring images paired with captions. WFH generates visual hallucinations from texts, which are then paired with the originally unpaired texts, allowing more diverse interactions across modalities. Empirically, WFH consistently boosts the prior W-VLP works, e.g. U-VisualBERT (U-VB), over a variety of V-L tasks, i.e. XMR, Visual Question Answering, etc. Notably, benchmarked with recall@{1,5,10}, it consistently improves U-VB on image-to-text and text-to-image retrieval on two popular datasets Flickr30K and MSCOCO. Meanwhile, it gains by at least 14.5% in cross-dataset generalization tests on these XMR tasks. Moreover, in other V-L downstream tasks considered, our WFH models are on par with models trained with paired V-L data, revealing the utility of unpaired data. These results demonstrate greater generalization of the proposed W-VLP model with WFH.Comment: Accepted to WACV'23. Please find supplementary material at https://drive.google.com/file/d/1SmCBGsUgkYLAhmK83RZqY03bq4j3214p/view?usp=sharin

TparvaDB: a database to support Theileria parva vaccine development

We describe the development of TparvaDB, a comprehensive resource to facilitate research towards development of an East Coast fever vaccine, by providing an integrated user-friendly database of all genome and related data currently available for Theileria parva. TparvaDB is based on the Generic Model Organism Database (GMOD) platform. It contains a complete reference genome sequence, Expressed Sequence Tags (ESTs), Massively Parallel Signature Sequencing (MPSS) expression tag data and related information from both public and private repositories. The Artemis annotation workbench provides online annotation functionality. TparvaDB represents a resource that will underpin and promote ongoing East Coast fever vaccine development and biological research

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page

Reframing Kurtz’s Painting: Colonial Legacies and Minority Rights in Ethnically Divided Societies

Minority rights constitute some of the most normatively and economically important human rights. Although the political science and legal literatures have proffered a number of constitutional and institutional design solutions to address the protection of minority rights, these solutions are characterized by a noticeable neglect of, and lack of sensitivity to, historical processes. This Article addresses that gap in the literature by developing a causal argument that explains diverging practices of minority rights protections as functions of colonial governments’ variegated institutional practices with respect to particular ethnic groups. Specifically, this Article argues that in instances where colonial governments politicize and institutionalize ethnic hegemony in the pre-independence period, an institutional legacy is created that leads to lower levels of minority rights protections. Conversely, a uniform treatment and depoliticization of ethnicity prior to independence ultimately minimizes ethnic cleavages post-independence and consequently causes higher levels of minority rights protections. Through a highly structured comparative historical analysis of Botswana and Ghana, this Article builds on a new and exciting research agenda that focuses on the role of long-term historio-structural and institutional influences on human rights performance and makes important empirical contributions by eschewing traditional methodologies that focus on single case studies that are largely descriptive in their analyses. Ultimately, this Article highlights both the strength of a historical approach to understanding current variations in minority rights protections and the varied institutional responses within a specific colonial government

Multiple light scattering in anisotropic random media

In the last decade Diffusing Wave Spectroscopy (DWS) has emerged as a powerful tool to study turbid media. In this article we develop the formalism to describe light diffusion in general anisotropic turbid media. We give explicit formulas to calculate the diffusion tensor and the dynamic absorption coefficient, measured in DWS experiments. We apply our theory to uniaxial systems, namely nematic liquid crystals, where light is scattered from thermal fluctuations of the local optical axis, called director. We perform a detailed analysis of the two essential diffusion constants, parallel and perpendicular to the director, in terms of Frank elastic constants, dielectric anisotropy, and applied magnetic field. We also point out the relevance of our results to different liquid crystalline systems, such as discotic nematics, smectic-A phases, and polymer liquid crystals. Finally, we show that the dynamic absorption coefficient is the angular average over the inverse viscosity, which governs the dynamics of director fluctuations.Comment: 23 pages, 12 ps figures, to be published in Phys. Rev.

Rationale, design and methods of the Study of Work and Pain (SWAP): a cluster randomised controlled trial testing the addition of a vocational advice service to best current primary care for patients with musculoskeletal pain (ISRCTN 52269669)

Background Musculoskeletal pain is a major contributor to short and long term work absence. Patients seek care from their general practitioner (GP) and yet GPs often feel ill-equipped to deal with work issues. Providing a vocational case management service in primary care, to support patients with musculoskeletal problems to remain at or return to work, is one potential solution but requires robust evaluation to test clinical and cost-effectiveness. Methods/Design This protocol describes a cluster randomised controlled trial, with linked qualitative interviews, to investigate the effect of introducing a vocational advice service into general practice, to provide a structured approach to managing work related issues in primary care patients with musculoskeletal pain who are absent from work or struggling to remain in work. General practices (n = 6) will be randomised to offer best current care or best current care plus a vocational advice service. Adults of working age who are absent from or struggling to remain in work due to a musculoskeletal pain problem will be invited to participate and 330 participants will be recruited. Data collection will be through patient completed questionnaires at baseline, 4 and 12 months. The primary outcome is self-reported work absence at 4 months. Incremental cost-utility analysis will be undertaken to calculate the cost per additional QALY gained and incremental net benefits. A linked interview study will explore the experiences of the vocational advice service from the perspectives of GPs, nurse practitioners (NPs), patients and vocational advisors. Discussion This paper presents the rationale, design, and methods of the Study of Work And Pain (SWAP) trial. The results of this trial will provide evidence to inform primary care practice and guide the development of services to provide support for musculoskeletal pain patients with work-related issues. Trial registration Current Controlled Trials ISRCTN52269669