Super-Resolution of Unmanned Airborne Vehicle Images with Maximum Fidelity Stochastic Restoration

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. One may, then, envision a scenario where a set of LR images is acquired with sensors on a moving platform like unmanned airborne vehicles (UAV). Due to the wind, the UAV may encounter altitude change or rotational effects which can distort the acquired as well as the processed images. Also, the visual quality of the SR image is affected by image acquisition degradations, the available number of the LR images and their relative positions. This dissertation seeks to develop a novel fast stochastic algorithm to reconstruct a single SR image from UAV-captured images in two steps. First, the UAV LR images are aligned using a new hybrid registration algorithm within subpixel accuracy. In the second step, the proposed approach develops a new fast stochastic minimum square constrained Wiener restoration filter for SR reconstruction and restoration using a fully detailed continuous-discrete-continuous (CDC) model. A new parameter that accounts for LR images registration and fusion errors is added to the SR CDC model in addition to a multi-response restoration and reconstruction. Finally, to assess the visual quality of the resultant images, two figures of merit are introduced: information rate and maximum realizable fidelity. Experimental results show that quantitative assessment using the proposed figures coincided with the visual qualitative assessment. We evaluated our filter against other SR techniques and its results were found to be competitive in terms of speed and visual quality

Galaxies Associated with z~4 Damped Lya Systems: I. Imaging and Photometric Selection

This paper describes the acquisition and analysis of imaging data for the identification of galaxies associated with z~4 damped Lya systems. We present deep BRI images of three fields known to contain four z~4 damped systems. We discuss the reduction and calibration of the data, detail the color criteria used to identify z~4 galaxies, and present a photometric redshift analysis to complement the color selection. We have found no galaxy candidates closer to the QSO than 7'' which could be responsible for the damped Lya systems. Assuming that at least one of the galaxies is not directly beneath the QSO, we set an upper limit on this damped Lya system of L < L*/4. Finally, we have established a web site to release these imaging data to the public.Comment: 12 pages, 6 embedded figures (3 color), 9 jpg figures. Higher quality ps versions of the images and the fits data are available at http://kingpin.ucsd.edu/~dlaimg, Accepted to the Astronomical Journal Jan 22, 200

Super-resolution for unregistered satellite images

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Automated system design for the efficient processing of solar satellite images. Developing novel techniques and software platform for the robust feature detection and the creation of 3D anaglyphs and super-resolution images for solar satellite images.

The Sun is of fundamental importance to life on earth and is studied by scientists from many disciplines. It exhibits phenomena on a wide range of observable scales, timescales and wavelengths and due to technological developments there is a continuing increase in the rate at which solar data is becoming available for study which presents both opportunities and challenges. Two satellites recently launched to observe the sun are STEREO (Solar TErrestrial RElations Observatory), providing simultaneous views of the SUN from two different viewpoints and SDO (Solar Dynamics Observatory) which aims to study the solar atmosphere on small scales and times and in many wavelengths. The STEREO and SDO missions are providing huge volumes of data at rates of about 15 GB per day (initially it was 30 GB per day) and 1.5 terabytes per day respectively. Accessing these huge data volumes efficiently at both high spatial and high time resolutions is important to support scientific discovery but requires increasingly efficient tools to browse, locate and process specific data sets. This thesis investigates the development of new technologies for processing information contained in multiple and overlapping images of the same scene to produce images of improved quality. This area in general is titled Super Resolution (SR), and offers a technique for reducing artefacts and increasing the spatial resolution. Another challenge is to generate 3D images such as Anaglyphs from uncalibrated pairs of SR images. An automated method to generate SR images is presented here. The SR technique consists of three stages: image registration, interpolation and filtration. Then a method to produce enhanced, near real-time, 3D solar images from uncalibrated pairs of images is introduced. Image registration is an essential enabling step in SR and Anaglyph processing. An accurate point-to-point mapping between views is estimated, with multiple images registered using only information contained within the images themselves. The performances of the proposed methods are evaluated using benchmark evaluation techniques. A software application called the SOLARSTUDIO has been developed to integrate and run all the methods introduced in this thesis. SOLARSTUDIO offers a number of useful image processing tools associated with activities highly focused on solar images including: Active Region (AR) segmentation, anaglyph creation, solar limb extraction, solar events tracking and video creation

Looking beyond Pixels:Theory, Algorithms and Applications of Continuous Sparse Recovery

Sparse recovery is a powerful tool that plays a central role in many applications, including source estimation in radio astronomy, direction of arrival estimation in acoustics or radar, super-resolution microscopy, and X-ray crystallography. Conventional approaches usually resort to discretization, where the sparse signals are estimated on a pre-defined grid. However, sparse signals do not line up conveniently on any grid in reality. While the discrete setup usually leads to a simple optimization problem that can be solved with standard tools, there are two noticeable drawbacks: (i) Because of the model mismatch, the effective noise level is increased; (ii) The minimum reachable resolution is limited by the grid step-size. Because of the limitations, it is essential to develop a technique that estimates sparse signals in the continuous-domain--in essence seeing beyond pixels. The aims of this thesis are (i) to further develop a continuous-domain sparse recovery framework based on finite rate of innovation (FRI) sampling on both theoretical and algorithmic aspects; (ii) adapt the proposed technique to several applications, namely radio astronomy point source estimation, direction of arrival estimation in acoustics, and single image up-sampling; (iii) show that the continuous-domain sparse recovery approach can surpass the instrument resolution limit and achieve super-resolution. We propose a continuous-domain sparse recovery technique by generalizing the FRI sampling framework to cases with non-uniform measurements. We achieve this by identifying a set of unknown uniform sinusoidal samples and the linear transformation that links the uniform samples of sinusoids to the measurements. The continuous-domain sparsity constraint can be equivalently enforced with a discrete convolution equation of these sinusoidal samples. The sparse signal is reconstructed by minimizing the fitting error between the given and the re-synthesized measurements subject to the sparsity constraint. Further, we develop a multi-dimensional sampling framework for Diracs in two or higher dimensions with linear sample complexity. This is a significant improvement over previous methods, which have a complexity that increases exponentially with dimension. An efficient algorithm has been proposed to find a valid solution to the continuous-domain sparse recovery problem such that the reconstruction (i) satisfies the sparsity constraint; and (ii) fits the measurements (up to the noise level). We validate the flexibility and robustness of the FRI-based continuous-domain sparse recovery in both simulations and experiments with real data. We show that the proposed method surpasses the diffraction limit of radio telescopes with both realistic simulation and real data from the LOFAR radio telescope. In addition, FRI-based sparse reconstruction requires fewer measurements and smaller baselines to reach a similar reconstruction quality compared with conventional methods. Next, we apply the proposed approach to direction of arrival estimation in acoustics. We show that accurate off-grid source locations can be reliably estimated from microphone measurements with arbitrary array geometries. Finally, we demonstrate the effectiveness of the continuous-domain sparsity constraint in regularizing an otherwise ill-posed inverse problem, namely single-image super-resolution. By incorporating image edge models, the up-sampled image retains sharp edges and is free from ringing artifacts

Overcoming resolution limits in fluorescence microscopy with adaptive optics and structured illumination

This thesis presents an investigation of two dynamic optical techniques for restoring and extending the spatial frequency response of fluorescence microscopes. In the first method, adaptive optics (AO), imaging performance is improved through the measurement and compensation of wavefront aberrations. The use of fluorescent guide stars contained within the sample is explored to allow direct wavefront sensing in a microscope. This guide star method is tested using an artificial phantom object and is applied to measure the wavefront aberrations induced by a commonly studied biological organism. It is shown that such a scheme can be used in combination with a confocal pinhole to reject out of focus light and allow effective wavefront sensing in thick biological samples. The direct wavefront sensing technique is then used in a closed loop AO system incorporated in a combined widefield and confocal fluorescence microscope. The design and validation of the microscope system are presented and the device is used for aberration corrected imaging of synthetic samples and a biological organism. Whilst AO makes possible the restoration of diffraction limited imaging performance, structured illumination microscopy (SIM) seeks to increase effective spatial resolution through frequency mixing between the sample and a spatially modulated excitation field. A high speed SIM system is presented in which the excitation patterns are generated using a liquid crystal on silicon spatial light modulator configured as a binary phase grating. The optical system and image reconstruction methods are described and the effect of light polarisation state on pattern formation is investigated using vectorial ray tracing and experimental measurements. The ability of the system to generate superresolution and optically sectioned images is tested using fluorescent microspheres and a range of biological samples.Open Acces

An automated image processing system for the detection of photoreceptor cells in adaptive optics retinal images

The rapid progress in Adaptive Optics (AO) imaging, in the last decades, has had a transformative impact on the entire approach underpinning the investigations of retinal tissues. Capable of imaging the retina in vivo at the cellular level, AO systems have revealed new insights into retinal structures, function, and the origins of various retinal pathologies. This has expanded the field of clinical research and opened a wide range of applications for AO imaging. The advances in image processing techniques contribute to a better observation of retinal microstructures and therefore more accurate detection of pathological conditions. The development of automated tools for processing images obtained with AO allows for objective examination of a larger number of images with time and cost savings and thus facilitates the use of AO imaging as a practical and efficient tool, by making it widely accessible to the clinical ophthalmic community. In this work, an image processing framework is developed that allows for enhancement of AO high-resolution retinal images and accurate detection of photoreceptor cells. The proposed framework consists of several stages: image quality assessment, illumination compensation, noise suppression, image registration, image restoration, enhancement and detection of photoreceptor cells. The visibility of retinal features is improved by tackling specific components of the AO imaging system, affecting the quality of acquired retinal data. Therefore, we attempt to fully recover AO retinal images, free from any induced degradation effects. A comparative study of different methods and evaluation of their efficiency on retinal datasets is performed by assessing image quality. In order to verify the achieved results, the cone packing density distribution was calculated and correlated with statistical histological data. From the performed experiments, it can be concluded that the proposed image processing framework can effectively improve photoreceptor cell image quality and thus can serve as a platform for further investigation of retinal tissues. Quantitative analysis of the retinal images obtained with the proposed image processing framework can be used for comparison with data related to pathological retinas, as well as for understanding the effect of age and retinal pathology on cone packing density and other microstructures