Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes

abstract: Digital imaging and image processing technologies have revolutionized the way in which we capture, store, receive, view, utilize, and share images. In image-based applications, through different processing stages (e.g., acquisition, compression, and transmission), images are subjected to different types of distortions which degrade their visual quality. Image Quality Assessment (IQA) attempts to use computational models to automatically evaluate and estimate the image quality in accordance with subjective evaluations. Moreover, with the fast development of computer vision techniques, it is important in practice to extract and understand the information contained in blurred images or regions. The work in this dissertation focuses on reduced-reference visual quality assessment of images and textures, as well as perceptual-based spatially-varying blur detection. A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The proposed method requires a very small number of reduced-reference (RR) features. Extensive experiments performed on different benchmark databases demonstrate that the proposed RRIQA method, delivers highly competitive performance as compared with the state-of-the-art RRIQA models for both natural and texture images. In the context of texture, the effect of texture granularity on the quality of synthesized textures is studied. Moreover, two RR objective visual quality assessment methods that quantify the perceived quality of synthesized textures are proposed. Performance evaluations on two synthesized texture databases demonstrate that the proposed RR metrics outperforms full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures. Last but not least, an effective approach to address the spatially-varying blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings is proposed. The evaluations of the proposed approach on a diverse sets of blurry images with different blur types, levels, and content demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Super resolution of B-mode ultrasound images with deep learning

Ultrasound offers a safe, non-invasive, and inexpensive way of imaging. However, due to its natural intrinsic imaging characteristics, it produces poor quality images with low resolution (LR) compared to other medical imaging modalities. Various image enhancement techniques have been extensively studied to overcome these shortcomings. Super-resolution (SR) is one of these methods, which endeavor to obtain high resolution (HR) images from LR images while enlarging them. Numerous studies have already utilized different SR techniques in various stages of ultrasound imaging (USI). Unlike other studies, which aimed at obtaining SR in the pre-processing phase or early stages of the post-processing phase of USI, we achieved SR on B-mode ultrasound images, which is the last stage of USI. We constructed a deep convolutional neural network (CNN) and trained it with a very large dataset of B-mode ultrasound images for the scale factors 2, 3, 4, and 8. We evaluated the performance of our proposed model quantitatively with eight image quality measures. The quantitative results revealed that our algorithm is much more successful than other methods at each magnification factor. Furthermore, we also verified that there is a statistically significant difference between our approach and others. Besides, qualitative analysis of the reconstructed images also confirms that it produces much better quality HR images than other methods in terms of the human visual system.Consejo Nacional de Investigaciones Cienta­ficas y Taccnicas 119E015 CONICE

Bayesian Variational Regularisation for Dark Matter Reconstruction with Uncertainty Quantification

Despite the great wealth of cosmological knowledge accumulated since the early 20th century, the nature of dark-matter, which accounts for ~85% of the matter content of the universe, remains illusive. Unfortunately, though dark-matter is scientifically interesting, with implications for our fundamental understanding of the Universe, it cannot be directly observed. Instead, dark-matter may be inferred from e.g. the optical distortion (lensing) of distant galaxies which, at linear order, manifests as a perturbation to the apparent magnitude (convergence) and ellipticity (shearing). Ensemble observations of the shear are collected and leveraged to construct estimates of the convergence, which can directly be related to the universal dark-matter distribution. Imminent stage IV surveys are forecast to accrue an unprecedented quantity of cosmological information; a discriminative partition of which is accessible through the convergence, and is disproportionately concentrated at high angular resolutions, where the echoes of cosmological evolution under gravity are most apparent. Capitalising on advances in probability concentration theory, this thesis merges the paradigms of Bayesian inference and optimisation to develop hybrid convergence inference techniques which are scalable, statistically principled, and operate over the Euclidean plane, celestial sphere, and 3-dimensional ball. Such techniques can quantify the plausibility of inferences at one-millionth the computational overhead of competing sampling methods. These Bayesian techniques are applied to the hotly debated Abell-520 merging cluster, concluding that observational catalogues contain insufficient information to determine the existence of dark-matter self-interactions. Further, these techniques were applied to all public lensing catalogues, recovering the then largest global dark-matter mass-map. The primary methodological contributions of this thesis depend only on posterior log-concavity, paving the way towards a, potentially revolutionary, complete hybridisation with artificial intelligence techniques. These next-generation techniques are the first to operate over the full 3-dimensional ball, laying the foundations for statistically principled universal dark-matter cartography, and the cosmological insights such advances may provide

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

Nanoscale Optical and Correlative Microscopies for Quantitative Characterization of DNA Nanostructures

Methods to engineer nanomaterials and devices with uniquely tailored properties are highly sought after in fields such as manufacturing, medicine, energy, and the environment. The macromolecule deoxyribonucleic acid (DNA) enables programmable self-assembly of nanostructures with near arbitrary shape and size and with unprecedented precision and accuracy. Additionally, DNA can be chemically modified to attach molecules and nanoparticles, providing a means to organize active materials into devices with unique or enhanced properties. One particularly powerful form of DNA-based self-assembly, DNA origami, provides robust structures with the potential for nanometer-scale resolution of addressable sites. DNA origami are assembled from one large DNA scaffold strand and many unique, short staple strands; each staple programmatically binds the scaffold at several distant domains, and the coordinated interactions of many staples with the scaffold act to fold the scaffold into a desired shape. The utility of DNA origami has been demonstrated through multiple applications, such as plasmonic and photonic devices, electronic device patterning, information storage, drug delivery, and biosensors. Despite the promise of DNA nanotechnology, few products have successfully translated from the laboratory to industry. Achieving high yield and high-precision synthesis of stable DNA nanostructures is one of the biggest challenges to applications of DNA nanostructures. For adoption in manufacturing, methods to measure and inspect assembled structures (i.e. metrology) are essential. Common high-resolution imaging techniques used to characterize DNA nanostructures, such as atomic force microscopy and transmission electron microscopy, cannot facilitate high-throughput characterization, and few studies have been directed towards the development of improved methods for nanoscale metrology. DNA-PAINT super-resolution microscopy enables high-resolution, multiplexed imaging of reactive sites on DNA nanostructures and offers the potential for inline optical metrology. In this work, nanoscale metrologies utilizing DNA-PAINT were developed for DNA nanostructures and applied to characterize DNA origami arrays and single site defects on DNA origami. For metrology of DNA origami arrays, an embedded, multiplexed optical super-resolution methodology was developed to characterize the periodic structure and defects of two-dimensional arrays. Images revealed the spatial arrangement of structures within the arrays, internal array defects, and grain boundaries between arrays, enabling the reconstruction of arrays from the images. The nature of the imaging technique is also highly compatible with statistical methods, enabling rapid statistical analysis of synthesis conditions. To obtain a greater understanding of DNA origami defects at the scale of individual strands, correlative super-resolution and atomic force microscopies were enabled through the development of a simple and flexible method to bind DNA origami directly to cover glass, simultaneously passivating the surface to single-stranded DNA. High-resolution, correlative microscopy was performed to characterize DNA origami, and spatial correlation in super-resolution optical and topographic images of 5 nm was achieved, validating correlative microscopy for single strand defect metrology. Investigations of single strand defects showed little correlation to structural defects on DNA origami, revealing that most site defects occur on strands that are present in the structure, contrary to prior reports. In addition, the results suggest that the structural stability of DNA origami was decreased by DNA-PAINT imaging. The presented work demonstrated the development and application of advanced characterization techniques for DNA nanostructures, which will accelerate fundamental research and applications of DNA nanotechnology

Form in Darkness: Linking Visual Cortex Structure With Spontaneous Neural Function

Spontaneous neural activity within visual cortex is synchronized at varying spatial scales, from the cytoarchitecural level of individual neurons to the coarse scale of whole regions. The neural basis of this synchronicity remains ambiguous. In this thesis, we focus on the role visual experience plays in organizing the spontaneous activity within the visual system. We start in Chapter 2 by creating a means by which to analyze homologous patches of cortex between sighted and blind individuals, as lack of vision precludes the use of traditional stimulus-driven mapping techniques. We find that anatomy alone could indeed predict the retinotopic organization of an individual\u27s striate cortex with an accuracy equivalent to the length of a typical mapping experiment. Chapter 3 applies this approach to analyze the organization of spontaneous signals within the striate cortex of blind and sighted subjects. We find that lack of visual experience produces a subtle change in the pattern of corticocortico correlations only between the hemispheres, and that these correlations are best modeled as function of cortical distance, not retinotopy. Chapter 4 expands our analysis to include areas V2 and V3. Here, we find that persistent visual experience supports network-level neural synchrony between spatially distributed cortical visual areas at both a coarse (regional) and fine (topographic) scale. Together, these results allow us model the organization of spontaneous activity in visual cortex as a combination of network signals linked to visual function and intrinsic signals coupled to structural connections. In the final chapter, we examine possible top-down mediators that may further modulate this network-level correlation. Minimal change in synchronicity is observed in a subject with a corpus callosotomy, suggesting the preeminence of bottom-up inputs. Taken together, this work advances our understanding of the origins of coherent spontaneous neural activity within visual cortex