Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 16 of 16 for “"Image Deblurring"”.
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Architectures for computational photography
… photography refers to a wide range of image capture and processing techniques that extend the capabilities of digital photography and allow users to take photographs that could not have been taken by a traditional camera. Since its inception less than a decade ago, the field today …
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Image enhancement methods and applications in computational photography
… and cutting-edge topic in applied optics, image sensors and image processing fields to go beyond the limitations of traditional photography. The innovations of computational photography allow the photographer not only merely to take an image, but also, more importantly, to perform …
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Energy-efficient circuits and systems for computational imaging and vision on mobile devices
Eighty five percent of images today are taken by cell phones. These images are not merely projections of light from the scene onto the camera sensor but result from a deep calculation. This calculation involves a number of computational imaging algorithms such as high dynamic range (HDR) imaging, …
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Modeling and Analysis of Optical Blur for Everyday Photography
Optical blur obscures image detail due to the camera's optics. The blur appearance depends on the scene's depth, wavelength, as well as the camera. The inference of such information --- optical calibration, image deblurring, and depth estimation -- is of great interest to computer vision. Existing …
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On fundamental computational barriers in the mathematics of information
… to basis pursuit, linear programming, lasso and image deblurring as well as finding an optimal neural network given training data. These results are somewhat paradoxical given the success that existing algorithms exhibit when tackling these problems with real world datasets and a substantial …
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A General Framework of Large-Scale Convex Optimization Using Jensen Surrogates and Acceleration Techniques
… applications including Sparse Linear Regression (Image Deblurring), Positron Emission Tomography, X-Ray Transmission Tomography, Logistic Regression, Sparse Logistic Regression and Automatic Relevance Determination for X-Ray Transmission Tomography.</p>
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wavelet domain inversion and joint deconvolution/interpolation of geophysical data
… This allows for sparsely sampled data to aid in image deblurring problems, or, conversely, noisy and blurred data to aid in sample interpolation. In order to overcome difficulties arising from high dimensionality, the solution must be derived in the correct framework and the structure of the …
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Energy allocation and transmission scheduling for wireless and space communications
… This allows for sparsely sampled data to aid in image deblurring problems, or, conversely, noisy and blurred data to aid in sample interpolation. In order to overcome difficulties arising from high dimensionality, the solution must be derived in the correct framework and the structure of the …
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Techniques for high-speed high-resolution in-vivo volumetric fluorescence imaging
… a diffractive optical element (DOE), (2) an image deblurring algorithm termed Deblurring by Pixel Reassignment (DPR). Together, these methods aim to expand the capabilities of optical microscopy for high-speed, high-resolution, and quantitative imaging across complex biological samples. In …
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Computational imaging and inverse techniques for high-resolution and instantaneous spectral imaging
… computational system and then digitally forming images of interest from multiplexed measurements by means of solving an inverse problem. In particular, in the first approach, a nonscanning spectral imaging technique is developed to enable performing spectroscopy over a two-dimensional …
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Cooperative mobile robot and manipulator system for autonomous manufacturing
… blurred frames by integrating an efficient image deblurring framework, which can be used for the phase of autonomous material transport. The conventional localization systems in manufacturing rely on external setups such as ArUco marker, lacking of sufficient flexibility to adapt to …
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Deconvolution and sparsity based image restoration
… sparse representation are the two key areas in image and signal processing. In this thesis the classical image restoration problem is addressed using these two modalities. Image restoration, such as deblurring, dnoising, and in-painting belongs to the class of ill-posed linear inverse problems, …
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Deconvolution and sparsity based image restoration
… sparse representation are the two key areas in image and signal processing. In this thesis the classical image restoration problem is addressed using these two modalities. Image restoration, such as deblurring, dnoising, and in-painting belongs to the class of ill-posed linear inverse problems, …
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Algorithms for Sparse and Low-Rank Optimization: Convergence, Complexity and Applications
… sensing enables one to recover a signal or image with fewer observations than the "length" of the signal or image, and thus provides potential breakthroughs in applications where data acquisition is costly. However, the potential impact of compressed sensing cannot be realized without …
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Point Spread Function Engineering for Scene Recovery
… combination of optics and processing to produce images that cannot be captured with traditional cameras. Over the last decade, a range of computational cameras have been proposed, which use various optics designs to encode and use computation to decode useful visual information. What is often …
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Numerical Methods for Separable Nonlinear Inverse Problems with Constraint and Low Rank
… astronomy, geoscience and so on. For example, image reconstruction and deblurring require the use of methods to solve inverse problems. Since the problems are subject to many factors and noise, we can't simply apply general inversion methods. Furthermore in the problems of interest, the number …