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 46 for “"image denoising"”.
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Image Denoising: Invertible and General Denoising Frameworks
… cameras has resulted in a massive number of images being taken every day. However, due to the limitations of sensors and environments such as light conditions, the images are usually contaminated by noise. Obtaining visually clean images are essential for the accuracy of downstream high-level …
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Image Denoising: Invertible and General Denoising Frameworks
… cameras has resulted in a massive number of images being taken every day. However, due to the limitations of sensors and environments such as light conditions, the images are usually contaminated by noise. Obtaining visually clean images are essential for the accuracy of downstream high-level …
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Unrolling of Graph Total Variation for Image Denoising
… learning have enabled effective solutions in image denoising, in general their implementations overly rely on training data and require tuning of a large parameter set. In this thesis, a hybrid design that combines graph signal filtering with feature learning is proposed. It utilizes …
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Advanced numerical methods for image denoising and segmentation
Image denoising is one of the most major steps in current image processing. It is a pre-processing step which aims to remove certain unknown, random noise from an image and obtain an image free of noise for further image processing, such as image segmentation. Image segmentation, as another branch …
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Improving Extreme Low-light Image Denoising via Residual Learning
… Many methods have been proposed for the task of image denoising, but they fail to work with the noise under extremely low light conditions. Recently, deep learning based approaches have been presented that have higher objective quality than traditional methods, but they usually have high …
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Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising
Digital image denoising is a widely know problem in image processing. In this work, we focus on removing additive white Gaussian noise from a given image. We use a denoising method that was extended from the Sparseland model. This method builds sparse image patch representations using redundant …
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Integrating Principal Component Analysis and Deep Learning Methods for Data Representation and Image Denoising
… thesis studies dimensionality reduction and denoising with a focus on Principal Component Analysis (PCA) and its nonlinear counterparts. The main aim is to review the PCA machinery and assess its effectiveness on real data and images. Two prototype tasks and a comparative denoising study are …
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Patch-based Denoising Algorithms for Single and Multi-view Images
In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is …
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Prior Information Guided Image Processing and Compressive Sensing
Signal/image processing and reconstruction based on mathematical modeling and computational techniques have been well developed and still attract much attention due to their broad applications. It becomes challenging to build mathematical models if the given data lacks some certainties. Prior …
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Deep learning for image restoration and enhancement
Image restoration is the process of recovering an original clean image from its degraded version, and image enhancement takes the goal of improving the image quality either objectively or subjectively. Both of them play a key part in computer vision and image processing and have broad applications …
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Interpretable Deep Image Denoiser by Unrolling Graph Laplacian Regularizer
Image denoising is a fundamental problem in image restoration. Researchers have studied the problem for decades and proposed numerous algorithms. In the past ten years, deep learning has produced complex models that deliver high-quality denoised images, but these models require on large numbers of …
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Image interpolation and denoising in discrete wavelet transform domain
Traditionally, processing a compressed image requires decompression first. Following the related manipulations, the processed image is compressed again for storage. To reduce the computational complexity and processing time, manipulating images in the transform domain, which is possible, is an …
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Algoritmo Non-Local Means e Otimizações.
Image denoising is an important part of digital image processing, and many approaches were proposed to enhance the visualization of images. The Non-Local Means algorithm has great results in noise removal, although its computational complexity is high, making the algorithm not viable for practical …
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A Novel Image Retrieval Strategy Based on VPD and Depth with Pre-Processing
… proposes a comprehensive working flow for image retrieval. It contains four components: denoising, restoration, color features extraction, and depth feature extraction. We propose a visual perceptual descriptor (VPD) to extract color features from an image. Gradient direction is calculated …
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Novel higher order regularisation methods for image reconstruction
… variation-based variational methods for digital image reconstruction. These methods are formulated in the context of Tikhonov regularisation. We focus on regularisation techniques in which the regulariser incorporates second order derivatives or a sophisticated combination of first and second …
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Development of super resolution techniques for finer scale remote sensing image mapping
… and results are shown to be excellent. Image registration is an important step for SR in which misalignment can be measured for each of many low resolution images; therefore, a new and computationally efficient image registration is developed for this particular application. This …
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Autoencoder-based image dimensionality reduction methods
In this thesis, we study how images can be represented in a more compact way that still captures their most important features and preserves the similarities and dissimilarities between the images. These compact representations of images, also known as ‘image encodings’, allow us to identify …
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Derivation, experimental verification, and applications of a new color image model
Image modeling is an important area of image processing. Good image models are useful, for example, in image restoration problems because they provide constraints that can be imposed on degraded images to retrieve better approximations of the original image. Many physical models of images are …
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Transform learning based image and video processing
… from data. Numerous applications such as image denoising, magnetic resonance image (MRI), and computed tomography (CT) reconstruction have been shown to benefit from a good adaptive sparse model. Recently, the sparsifying transform model has received interest, for which sparse coding is …
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