Sparse Representation Of Visual Data For Compression And Compressed Sensing
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Sparse Representation Of Visual Data For Compression And Compressed Sensing
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Author : Ehsan Miandji
language : en
Publisher: Linköping University Electronic Press
Release Date : 2018-11-23
Sparse Representation Of Visual Data For Compression And Compressed Sensing written by Ehsan Miandji and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-23 with categories.
The ongoing advances in computational photography have introduced a range of new imaging techniques for capturing multidimensional visual data such as light fields, BRDFs, BTFs, and more. A key challenge inherent to such imaging techniques is the large amount of high dimensional visual data that is produced, often requiring GBs, or even TBs, of storage. Moreover, the utilization of these datasets in real time applications poses many difficulties due to the large memory footprint. Furthermore, the acquisition of large-scale visual data is very challenging and expensive in most cases. This thesis makes several contributions with regards to acquisition, compression, and real time rendering of high dimensional visual data in computer graphics and imaging applications. Contributions of this thesis reside on the strong foundation of sparse representations. Numerous applications are presented that utilize sparse representations for compression and compressed sensing of visual data. Specifically, we present a single sensor light field camera design, a compressive rendering method, a real time precomputed photorealistic rendering technique, light field (video) compression and real time rendering, compressive BRDF capture, and more. Another key contribution of this thesis is a general framework for compression and compressed sensing of visual data, regardless of the dimensionality. As a result, any type of discrete visual data with arbitrary dimensionality can be captured, compressed, and rendered in real time. This thesis makes two theoretical contributions. In particular, uniqueness conditions for recovering a sparse signal under an ensemble of multidimensional dictionaries is presented. The theoretical results discussed here are useful for designing efficient capturing devices for multidimensional visual data. Moreover, we derive the probability of successful recovery of a noisy sparse signal using OMP, one of the most widely used algorithms for solving compressed sensing problems.
Data Driven Reflectance Acquisition And Modeling For Predictive Rendering
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Author : Behnaz Kavoosighafi
language : en
Publisher: Linköping University Electronic Press
Release Date : 2025-05-22
Data Driven Reflectance Acquisition And Modeling For Predictive Rendering written by Behnaz Kavoosighafi and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-22 with categories.
Recent developments in computer graphics, and particularly within predictive rendering, have enabled highly realistic simulations of object appearances. While physically-based reflectance (PBR) models offer widespread utility, measured material reflectance data yields significantly higher accuracy through the direct empirical observation of complex light-scattering interactions. Nevertheless, acquiring and modeling reflectance data entails substantial computational overhead. This thesis investigates data-driven approaches to improve the acquisition, representation, and rendering of reflectance data, with a focus on predictive rendering to achieve precise and reliable visual simulations. The first part of the thesis focuses on acquisition of Bidirectional Reflectance Distribution Function (BRDF) and Spatially Varying BRDF (SVBRDF)—functions that describe light-surface interactions at each point based on incoming and reflected light directions. Lightweight setups are initially explored to enable efficient SVBRDF capture; however, their accuracy falls short for predictive rendering applications, motivating the adoption of gonioreflectometer-based setups. To improve measurement efficiency of such setups, a compressed sensing framework is introduced, which incorporates a deterministic sampling strategy. Additionally, a unified formulation for sparse BRDF acquisition is presented, allowing for the adaptation of sampling patterns and sample counts to the unique properties of each material. This approach significantly enhances reconstruction quality while preserving the same sampling budget. The second part of the thesis addresses modeling of reflectance measurements, particularly the Bidirectional Texture Function (BTF) and BRDF. Sparse representation techniques applied to existing BTF datasets prove effective in compressing texture data while enabling real-time rendering of the measured BTFs. Despite these advances, a discrepancy often arises between model-space errors introduced during approximation and the image-space errors perceived in rendered outputs. To bridge this gap, a systematic psychophysical experiment is performed to analyze the impact of BRDF modeling techniques on rendered material quality. Building on these findings, a neural metric is developed to evaluate perceptual accuracy directly in BRDF-space. This metric exhibits strong correlation with subjective human evaluations and presents the potential to guide BRDF fitting algorithms toward solutions that produce visually accurate and compelling renderings of real-world materials.
Computational Photography
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Author : Saghi Hajisharif
language : en
Publisher: Linköping University Electronic Press
Release Date : 2020-02-18
Computational Photography written by Saghi Hajisharif and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-18 with categories.
The introduction and recent advancements of computational photography have revolutionized the imaging industry. Computational photography is a combination of imaging techniques at the intersection of various fields such as optics, computer vision, and computer graphics. These methods enhance the capabilities of traditional digital photography by applying computational techniques both during and after the capturing process. This thesis targets two major subjects in this field: High Dynamic Range (HDR) image reconstruction and Light Field (LF) compressive capturing, compression, and real-time rendering. The first part of the thesis focuses on the HDR images that concurrently contain detailed information from the very dark shadows to the brightest areas in the scenes. One of the main contributions presented in this thesis is the development of a unified reconstruction algorithm for spatially variant exposures in a single image. This method is based on a camera noise model, and it simultaneously resamples, reconstructs, denoises, and demosaics the image while extending its dynamic range. Furthermore, the HDR reconstruction algorithm is extended to adapt to the local features of the image, as well as the noise statistics, to preserve the high-frequency edges during reconstruction. In the second part of this thesis, the research focus shifts to the acquisition, encoding, reconstruction, and rendering of light field images and videos in a real-time setting. Unlike traditional integral photography, a light field captures the information of the dynamic environment from all angles, all points in space, and all spectral wavelength and time. This thesis employs sparse representation to provide an end-to-end solution to the problem of encoding, real-time reconstruction, and rendering of high dimensional light field video data sets. These solutions are applied on various types of data sets, such as light fields captured with multi-camera systems or hand-held cameras equipped with micro-lens arrays, and spherical light fields. Finally, sparse representation of light fields was utilized for developing a single sensor light field video camera equipped with a color-coded mask. A new compressive sensing model is presented that is suitable for dynamic scenes with temporal coherency and is capable of reconstructing high-resolution light field videos.
Machine Learning And Visual Perception
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Author : Baochang Zhang
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2020-07-06
Machine Learning And Visual Perception written by Baochang Zhang and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-06 with Computers categories.
The book provides an up-to-date on machine learning and visual perception, including decision tree, Bayesian learning, support vector machine, AdaBoost, object detection, compressive sensing, deep learning, and reinforcement learning. Both classic and novel algorithms are introduced. With abundant practical examples, it is an essential reference to students, lecturers, professionals, and any interested lay readers.
Image Understanding Using Sparse Representations
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Author : Jayaraman J. Thiagarajan
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Image Understanding Using Sparse Representations written by Jayaraman J. Thiagarajan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.
Sparse Representation Modeling And Learning In Visual Recognition
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Author : Hong Cheng
language : en
Publisher: Springer
Release Date : 2015-05-25
Sparse Representation Modeling And Learning In Visual Recognition written by Hong Cheng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-25 with Computers categories.
This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.
Neural Networks And Statistical Learning
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Author : Ke-Lin Du
language : en
Publisher: Springer Nature
Release Date : 2019-09-12
Neural Networks And Statistical Learning written by Ke-Lin Du and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-12 with Mathematics categories.
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Coded Optical Imaging
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Author : Jinyang Liang
language : en
Publisher: Springer Nature
Release Date : 2024-03-19
Coded Optical Imaging written by Jinyang Liang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-19 with Science categories.
This book provides a comprehensive survey of coded optical imaging. Illustrated with 386 figures, it takes readers from the fundamental concepts and theories to the latest research and applications in this field. It can be used in graduate-level courses in optics and photonics. It can also benefit scientists and engineers in optical imaging, computer graphics, and other related disciplines. This book starts from a brief history of coded optical imaging and key operations in its data acquisition and image reconstruction. It then presents the latest progress in technological development and applications in the areas of biomedicine, materials science, industrial inspection, optical physics, imaging science, information theory, and more. Chapters describe the most representative techniques, exposing readers to key research themes, including: · Optical signal encoding · Image reconstruction techniques · Compressed sensing · Artificial intelligence · Metasurface · Structured light · Lensless imaging · Holography · Tomography · Light-field imaging · Utrafast imaging · Hyperspectral imaging · Polarization imaging · Super-resolution imaging
Sparse And Redundant Representations
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Author : Michael Elad
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-08-12
Sparse And Redundant Representations written by Michael Elad and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-08-12 with Mathematics categories.
A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.
Sparse Modeling For Image And Vision Processing
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Author : Julien Mairal
language : en
Publisher: Now Publishers
Release Date : 2014-12-19
Sparse Modeling For Image And Vision Processing written by Julien Mairal and has been published by Now Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-19 with Computers categories.
Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.