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Identifier 000429151
Title Sparse representations and coupled dictionary learning for the enhancement of computational imaging systems
Alternative Title Μέθοδοι αραιών αναπαραστάσεων και από κοινού εκμάθησης λεξικών για την ενίσχυση συστημάτων υπολογιστικής φωτογραφίας
Author Φωτιάδου, Κωνσταντίνα Π
Thesis advisor Τσακαλίδης, Παναγιώτης
Reviewer Αργυρός, Αντώνιος
Παπαδοπούλη, Μαρία
Ζαχαριάδης, Θεόδωρος
Starck, Jean-Luc
Χαρμανδάρης, Βασίλης
Ροντογιάννης, Αθανάσιος
Abstract The rapid evolution of display technologies and imaging sensors has raised an enormous interest in the imaging and multimedia communities, and thus the demand for providing highquality images has grown tremendously. Computational photography and image processing aim to improve the interpretability and perception of imaging content in order to facilitate subsequent tasks, such as automated analysis, detection, segmentation and recognition, among others. Despite the significant increase in the spatial resolution and the introduction of 3D content, spectral resolution, dynamic range enhancement, image denoising and deconvolution remain attractive research problems, as there is still room for improvement. The key contribution of this thesis is the design of novel post-acquisition, machine learning formulations that tackle the main limitations of imaging sensors, with special emphasis on satellite imaging technologies and multi-and hyperspectral imaging systems. In this dissertation, we propose novel and robust mathematical Coupled Dictionary Learning formulations, based on the intelligent scheme of the Alternating Direction Method of Multipliers (ADMM). We aim to learn dictionaries for sparse modeling in joint feature spaces while enforcing the desired relationships among the different signal representations. The proposed algorithmic formulations are able to overcome the main limitations of traditional Coupled Dictionary Learning schemes. Thus we are addressing a twofold problem. First, we extend the existing methodology of Sparse Representations and Coupled Dictionary Learning which is very important from an engineering point of view. On the other hand, we are assisting the research community which has started to appreciate the necessity of coupled feature spaces in several signal and image processing tasks, from image super-resolution, deconvolution and dynamic range synthesis of modern sensors, to hyperspectral and satellite image enhancement to further investigate this research area with novel algorithmic formulations. The structure of this dissertation goes as follows. Primarily, we address the problem of dynamic range enhancement of standard 8bit imagery. High Dynamic Range (HDR) imaging technology is acknowledged as the next success story in consumer imaging, with a vastly increasing image and video acquisition, content reproduction, and graphics applications supporting HDR features. We produce HDR content from a single image using an innovative Machine Learning (ML) approach that first generates a sequence of bracketed exposures via a Joint Dictionary Learning formulation, encoding the transformations between differently exposed images, and then combines them ideally into a well-illuminated low dynamic range (LDR) image or merges them directly into a HDR image. Additionally, we learn appropriate features by employing a stacked sparse autoencoder (SSAE) based framework. Second, we confront one of the major limitations of multi- and hyperspectral imagery: the spectral resolution enhancement. The majority of literature approaches mostly focuses on the enhancement of spatial resolution, and thus only a handful of techniques tackle the problems of spectral and spatio-spectral resolution enhancement of satellite sensors. In this dissertation, we develop a coupled dictionary learning model which considers joint feature spaces, composed of low and high spectral resolution hyper-cubes, in order to achieve spectral super-resolution performance. We formulate our spectral coupled dictionary learning optimization problem within the context of the Alternating Direction Method of Multipliers, and we manage to update the involved quantities via closed-form expressions. Moreover, we investigate the core problem of spectroscopic data denoising on simulated Euclid-like noisy templates, and we exploit the proposed ADMM Coupled Dictionary Learning methodology in order to learn coupled feature spaces, composed of high- and low-quality spectral profiles. The reconstructed spectral profiles are able to improve the accuracy, reliability and robustness of automated redshift estimation methods. Additionally, another significant contribution of this dissertation includes the deconvolution of astronomical imagery. Specifically, we design a novel and robust post-acquisition formulation that recovers the high-quality versions of blurry astronomical observations, along with information details regarding the blur kernel, i.e. point spread function (PSF). In order to accomplish this goal, we exploit the mathematical frameworks of Sparse Representations, and the Alternating Direction Method of Multipliers. Likewise, we apply our proposed ADMM Coupled Dictionary Learning scheme on a highly challenging problem of the remote sensing community. Specifically, we consider the retrieval of the active measurements of the Soil Moisture Active Passive (SMAP) satellite. The upper goal of this study is the direct soil moisture estimation from SMAP's satellite measurements. In the third part of this dissertation, we consider a novel hyperspectral image understanding technique. Specifically, we propose a novel machine learning technique that addresses the hyperspectral image classification problem by employing the state-of-the-art scheme of Convolutional Neural Networks (CNNs). The formal approach introduced in this work exploits the fact that the spatio-spectral information of an input scene can be encoded via CNNs and combined with multi-class classifiers. The proposed deep feature learning scheme is focused on the classification of snapshot mosaic hyperspectral imagery, while a new hyperspectral classification dataset of indoor scenes is constructed. Last but not least, we have conducted a complete investigation of the experimental setups with multiple real-world's and synthetic datasets and compare it with the best state-of-theart algorithms.
Language English
Subject Alternating direction Method of Multipliers
Astronomical Image Deconvolution
Astronomical Signal Denoising
High Dynamic Range Imaging Systems
Hyperspectral Imaging
Satellite Imaging
Soil Moisture Retrieval
Ανάκτηση Επιπέδων Υγρασίας του Εδάφους
Αποσυνέλιξη Αστρονομικών Εικόνων
Αραιές Αναπαραστάσεις
Αφαίρεση Θορύβου από Αστρονομικά Σήματα
Δορυφορική Επεξεργασία Εικόνων
Πολυφασματική Επεξεργασία Εικόνων
Τεχνική Εναλλακτικής Κατεύθυνησης Πολλαπλασιαστών Λαγκράνζ
Υπολογιστική Φωτογραφία
Υψηλού ∆υναμικού Εύρους Συστήματα Εικόνων
Issue date 2020-03-27
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
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