An online algorithm for separating sparse and low-dimensional signal sequences from their sum, and its applications in video processing

dc.contributor.advisor Namrata Vaswani
dc.contributor.author Guo, Han
dc.contributor.department Electrical and Computer Engineering
dc.date 2019-11-04T21:48:47.000
dc.date.accessioned 2020-06-30T03:18:34Z
dc.date.available 2020-06-30T03:18:34Z
dc.date.copyright Thu Aug 01 00:00:00 UTC 2019
dc.date.embargo 2001-01-01
dc.date.issued 2019-01-01
dc.description.abstract <p>In signal processing, ``low-rank + sparse'' is an important assumption when separating two signals from their sum. Many applications, e.g., video foreground/background separation are well-formulated by this assumption. In this work, with the ``low-rank + sparse'' assumption, we design and evaluate an online algorithm, called practical recursive projected compressive sensing (prac-ReProCS) for recovering a time sequence of sparse vectors St and a time sequence of dense vectors Lt from their sum, Mt = St + Lt, when the Lt's lie in a slowly changing low-dimensional subspace of the full space.</p> <p>In the first part of this work (Chapter 1-5), we study and discuss the prac-ReProCS algorithm, the practical version of the original ReProCS algorithm. We apply prac-ReProCS to a key application -- video layering, where the goal is to separate a video sequence into a slowly changing background sequence and a sparse foreground sequence that consists of one or more moving regions/objects on-the-fly. Via experiments we show that prac-ReProCS has significantly better performance compared with other state-of-the-art robust-pca methods when applied to video foreground-background separation.</p> <p>In the second part of this work (Chapter 6), we study the problem of video denoising. We apply prac-ReProCS to video denoising as a preprocessing step. We develop a novel approach to video denoising that is based on the idea that many noisy or corrupted videos can be split into three parts -- the ``low-rank laye'', the ``sparse layer'' and a small residual which is small and bounded. We show using extensive experiments, layering-then-denoising is effective, especially for long videos with small-sized images that those corrupted by general large variance noise or by large sparse noise, e.g., salt-and-pepper noise.</p> <p>In the last part of this work (Chapter 7), we discuss an independent problem called logo detection and propose a future research direction where prac-ReProCS can be combined with deep learning solutions.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/17457/
dc.identifier.articleid 8464
dc.identifier.contextkey 15681449
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/17457
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/31640
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/17457/Guo_iastate_0097E_18273.pdf|||Fri Jan 14 21:23:32 UTC 2022
dc.subject.disciplines Engineering
dc.subject.keywords compressive sensing
dc.subject.keywords low-rank
dc.subject.keywords robust PCA
dc.subject.keywords sparse recovery
dc.subject.keywords video denoising
dc.subject.keywords video layering
dc.title An online algorithm for separating sparse and low-dimensional signal sequences from their sum, and its applications in video processing
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.discipline Electrical Engineering
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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