Compressed Sensing Reconstruction Using Structural Dependency Models

Persistent Link:
http://hdl.handle.net/10150/238613
Title:
Compressed Sensing Reconstruction Using Structural Dependency Models
Author:
Kim, Yookyung
Issue Date:
2012
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Compressed sensing (CS) theory has demonstrated that sparse signals can be reconstructed from far fewer measurements than suggested by the Nyquist sampling theory. CS has received great attention recently as an alternative to the current paradigm of sampling followed by compression. Initial CS operated under the implicit assumption that the sparsity domain coefficients are independently distributed. Recent results, however, show that exploiting statistical dependencies in sparse signals improves the recovery performance of CS. This dissertation proposes model-based CS reconstruction techniques. Statistical dependency models for several CS problems are proposed and incorporated into different CS algorithms. These models allow incorporation of a priori information into the CS reconstruction problems. Firstly, we propose the use of a Bayes least squares-Gaussian scale mixtures (BLS-GSM) model for CS recovery of natural images. The BLS-GSM model is able to exploit dependencies inherent in wavelet coefficients. This model is incorporated into several recent CS algorithms. The resulting methods significantly reduce reconstruction errors and/or the number of measurements required to obtain a desired reconstruction quality, when compared to state-of-the-art model-based CS methods in the literature. The model-based CS reconstruction techniques are then extended to video. In addition to spatial dependencies, video sequences exhibit significant temporal dependencies as well. In this dissertation, a model for jointly exploiting spatial and temporal dependencies in video CS is also proposed. The proposed method enforces structural self-similarity of image blocks within each frame as well as across neighboring frames. By sparsely representing collections of similar blocks, dominant image structures are retained while noise and incoherent undersampling artifacts are eliminated. A new video CS algorithm which incorporates this model is then proposed. The proposed algorithm iterates between enforcement of the self-similarity model and consistency with measurements. By enforcing measurement consistency in residual domain, sparsity is increased and CS reconstruction performance is enhanced. The proposed approach exhibits superior subjective image quality and significantly improves peak-signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).Finally, a model-based CS framework is proposed for super resolution (SR) reconstruction. The SR reconstruction is formulated as a CS problem and a self-similarity model is incorporated into the reconstruction. The proposed model enforces similarity of collections of blocks through shrinkage of their transform-domain coefficients. A sharpening operation is performed in transform domain to emphasize edge recovery. The proposed method is compared with state-of-the-art SR techniques and provides high-quality SR images, both quantitatively and subjectively.
Type:
text; Electronic Dissertation
Keywords:
model; reconstruction; sparsity; Electrical & Computer Engineering; compressed sensing; dependency
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical & Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Bilgin, Ali

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleCompressed Sensing Reconstruction Using Structural Dependency Modelsen_US
dc.creatorKim, Yookyungen_US
dc.contributor.authorKim, Yookyungen_US
dc.date.issued2012-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractCompressed sensing (CS) theory has demonstrated that sparse signals can be reconstructed from far fewer measurements than suggested by the Nyquist sampling theory. CS has received great attention recently as an alternative to the current paradigm of sampling followed by compression. Initial CS operated under the implicit assumption that the sparsity domain coefficients are independently distributed. Recent results, however, show that exploiting statistical dependencies in sparse signals improves the recovery performance of CS. This dissertation proposes model-based CS reconstruction techniques. Statistical dependency models for several CS problems are proposed and incorporated into different CS algorithms. These models allow incorporation of a priori information into the CS reconstruction problems. Firstly, we propose the use of a Bayes least squares-Gaussian scale mixtures (BLS-GSM) model for CS recovery of natural images. The BLS-GSM model is able to exploit dependencies inherent in wavelet coefficients. This model is incorporated into several recent CS algorithms. The resulting methods significantly reduce reconstruction errors and/or the number of measurements required to obtain a desired reconstruction quality, when compared to state-of-the-art model-based CS methods in the literature. The model-based CS reconstruction techniques are then extended to video. In addition to spatial dependencies, video sequences exhibit significant temporal dependencies as well. In this dissertation, a model for jointly exploiting spatial and temporal dependencies in video CS is also proposed. The proposed method enforces structural self-similarity of image blocks within each frame as well as across neighboring frames. By sparsely representing collections of similar blocks, dominant image structures are retained while noise and incoherent undersampling artifacts are eliminated. A new video CS algorithm which incorporates this model is then proposed. The proposed algorithm iterates between enforcement of the self-similarity model and consistency with measurements. By enforcing measurement consistency in residual domain, sparsity is increased and CS reconstruction performance is enhanced. The proposed approach exhibits superior subjective image quality and significantly improves peak-signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).Finally, a model-based CS framework is proposed for super resolution (SR) reconstruction. The SR reconstruction is formulated as a CS problem and a self-similarity model is incorporated into the reconstruction. The proposed model enforces similarity of collections of blocks through shrinkage of their transform-domain coefficients. A sharpening operation is performed in transform domain to emphasize edge recovery. The proposed method is compared with state-of-the-art SR techniques and provides high-quality SR images, both quantitatively and subjectively.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectmodelen_US
dc.subjectreconstructionen_US
dc.subjectsparsityen_US
dc.subjectElectrical & Computer Engineeringen_US
dc.subjectcompressed sensingen_US
dc.subjectdependencyen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBilgin, Alien_US
dc.contributor.committeememberMarcellin, Michael W.en_US
dc.contributor.committeememberDjordjevic, Ivanen_US
dc.contributor.committeememberBilgin, Alien_US
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