dc.creator | Trevisi, Marco | es |
dc.creator | Akbari, Ali | es |
dc.creator | Trocan, Maria | es |
dc.creator | Rodríguez Vázquez, Ángel Benito | es |
dc.creator | Carmona Galán, Ricardo | es |
dc.date.accessioned | 2020-03-09T14:39:08Z | |
dc.date.available | 2020-03-09T14:39:08Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Trevisi, M., Akbari, A., Trocan, M., Rodríguez Vázquez, Á.B. y Carmona Galán, R. (2020). Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction. IEEE Transactions on Circuits and Systems for Video Technology, 30 (2), 387-399. | |
dc.identifier.issn | 1051-8215 | es |
dc.identifier.issn | 1558-2205 | es |
dc.identifier.uri | https://hdl.handle.net/11441/94043 | |
dc.description.abstract | In this paper, we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in CS CMOS image sensors are recursive pseudo-random binary matrices. We have proved that the restricted isometry property of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random number generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behavior that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber reconstruction algorithm. By means of single value decomposition, we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TEC2015-66878-C3-1-R | es |
dc.description.sponsorship | Junta de Andalucía TIC 2338-2013 | es |
dc.description.sponsorship | Office of Naval Research (USA) N000141410355 | es |
dc.description.sponsorship | European Union H2020 765866 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 p. | es |
dc.language.iso | eng | es |
dc.publisher | Institute of Electrical and Electronics Engineers | es |
dc.relation.ispartof | IEEE Transactions on Circuits and Systems for Video Technology, 30 (2), 387-399. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | CMOS image sensor architecture | es |
dc.subject | Landweber reconstruction | es |
dc.subject | Power spectral density | es |
dc.subject | Compressive sensing | es |
dc.subject | Random binary matrix RIP proof | es |
dc.subject | Single value decomposition | es |
dc.subject | Ternary measurement matrix | es |
dc.title | Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo | es |
dc.relation.projectID | TEC2015-66878-C3-1-R | es |
dc.relation.projectID | TIC 2338-2013 | es |
dc.relation.projectID | N000141410355 | es |
dc.relation.projectID | 765866 | es |
dc.relation.publisherversion | https://doi.org/10.1109/TCSVT.2019.2892178 | es |
dc.identifier.doi | 10.1109/TCSVT.2019.2892178 | es |
dc.journaltitle | IEEE Transactions on Circuits and Systems for Video Technology | es |
dc.publication.volumen | 30 | es |
dc.publication.issue | 2 | es |
dc.publication.initialPage | 387 | es |
dc.publication.endPage | 399 | es |