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 20 of 30 for “"image super-resolution"”.
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Image super-resolution via sparse representation
This thesis presents a new approach to single-image super-resolution (SR), based on sparse signal recovery. Research on image statistics suggests that image patches can be well represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by …
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Learning image super resolution from joint examples
Image super-resolution (SR) aims to estimate of a high-resolution (HR) image from low-resolution (LR) input. Image priors are commonly learned to regularize the ill-posed SR problem, either using external LR-HR pairs or internal similar patterns repeating across di erent scales. We propose joint SR …
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Single Image Super-Resolution via Deep Dense Network
Image Super-Resolution (SR) is a research field of computer vision, which enhances the resolution of an imaging system. The need for high resolution is common in computer vision applications for better performance in pattern recognition and analysis of images. However, recovering of the HR image …
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Deep neural networks for medical image super-resolution
Super-resolution plays an essential role in medical imaging because it provides an alternative way to achieve high spatial resolutions with no extra acquisition cost. In the past decades, the rapid development of deep neural networks has ensured high reconstruction fidelity and photo-realistic …
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Vision task driven image super-resolution and image enhancement
… problems, low-light exposure, and low-quality images present great challenges in a variety of navigation and surveillance use cases. Recent advancements in deep learning-based methods may contribute towards the enhancement of low-light images to high-quality images with enough exposure. …
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A Joint Dictionary-Based Single-Image Super-Resolution Model
Image super-resolution technique mainly aims at restoring high-resolution image with satisfactory novel details. In recent years, leaning-based single-image super-resolution has been developed and proved to produce satisfactory results. With one or some dictionaries trained from a training set, …
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Image matching and image super-resolution via deep learning
… alternative to traditional CAD-based and image-based approaches for 3D modeling. The output of the 3D depth sensors is generally 3D color point cloud and LIDAR images which is collected at the time of the LIDAR survey. However, in order to model 3D scenes under different conditions …
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Single magnetic resonance image super-resolution using generative adversarial network
Super-Resolution is the process of converting given low-resolution images into corresponding high-resolution ones. The resolution enhancement process applied to medical images can potentially improve diagnostic accuracy for a variety of conditions and reduce imaging scan time. Recent improvements …
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Exploring the Internal Statistics: Single Image Super-Resolution, Completion and Captioning
<p>Image enhancement has drawn increasingly attention in improving image quality or interpretability. It aims to modify images to achieve a better perception for human visual system or a more suitable representation for further analysis in a variety of applications such as medical imaging, remote …
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Deep Convolutional Neural Networks Based Single Image Super-Resolution And Classification For Crater Detection
… crater locations are hypothesized using unsupervised algorithms. The validity of the hypothesized crater locations is then tested in a HV step using sophisticated supervised algorithms. Although many research efforts have been carried out in the field of crater detection, existing crater …
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Super-resolution image reconstruction from low-resolution images
… thesis addresses the problem of obtaining high-resolution image from a set of one or more low-resolution images. The thesis focused on three building blocks of super-resolution algorithms i.e., image registration for super-resolution, image fusion for super-resolution and super-resolution image …
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Learning to super-resolve images using self-similarities
The single image super-resolution problem entails estimating a high-resolution version of a low-resolution image. Recent studies have shown that high resolution versions of the patches of a given low-resolution image are likely to be found within the given image itself. This recurrence of patches …
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Computational photography with novel camera sensors
… high dynamic range (HDR) imaging and image superresolution (SR). HDR imaging refers to capturing both bright and dark details in the scenes simultaneously. A modulo camera does not get saturated during exposure, enabling HDR photography in a single shot without losing spatial …
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Sparse Representations and Nonlinear Image Processing for Inverse Imaging Solutions
… applies sparse representations and nonlinear image processing to two inverse imaging problems. The first problem involves image restoration, where the aim is to reconstruct an unknown high-quality image from a low-quality observed image. Sparse representations of images have drawn a …
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Hardware Acceleration of a Neighborhood Dependent Component Feature Learning (NDCFL) Super-Resolution Algorithm
Image processing and computer vision algorithms allow computers to make sense of pictures and video seen through cameras. These have applications in a large variety of “real time” applications like surveillance, intelligence gathering, robotics, automobile driving, aviation, etc., where the picture …
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Frequency-domain deconvolution in deep learning
… to two quintessential computer vision problems: image classification and single image super resolution (SISR). The results demonstrate the deconvolution layer's potential in certain scenarios, with marked improvements observed in image classification. For SISR tasks, though advantages were …
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Sparse modeling of high-dimensional data for learning and vision
… for various learning and vision tasks, including image classification, single image super-resolution, compressive sensing, and graph learning. Based on the bag-of-features (BoF) image representation in a spatial pyramid, we first transform each local image descriptor into a sparse representation, …
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Structure guided image restoration : a deep learning approach
Image restoration aims at recovery of degraded images and estimating the original. Over the past few years, computer vision research has been dominated by deep learning techniques in part due to advances in computing infrastructure, algorithms and image capturing devices. As a result, deep neural …
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Images in motion?: a first look into video leakage in federated learning
… While the impact of these attacks is known for image, text, and tabular data, their effect on video data remains an unexamined area of research. This paper presents the first analysis of video data leakage in FL via gradient inversion attacks. We evaluate two common video classification …
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