GPU efficient SAR image despeckling using mixed norms

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dc.authoridNAR, Fatih/0000-0002-3003-8136
dc.authoridOzcan, Caner/0000-0002-2854-4005
dc.authoridSEN, BAHA/0000-0003-3577-2548
dc.contributor.authorOzcan, Caner
dc.contributor.authorSen, Baha
dc.contributor.authorNar, Fatih
dc.date.accessioned2024-09-29T16:04:43Z
dc.date.available2024-09-29T16:04:43Z
dc.date.issued2014
dc.departmentKarabük Üniversitesien_US
dc.descriptionConference on High-Performance Computing in Remote Sensing IV -- SEP 22-23, 2014 -- Amsterdam, NETHERLANDSen_US
dc.description.abstractSpeckle noise which is inherent to Synthetic Aperture Radar (SAR) imaging obstructs various image exploitation tasks such as edge detection, segmentation, change detection, and target recognition. Therefore, speckle reduction is generally used as a first step which has to smooth out homogeneous regions while preserving edges and point scatterers. Traditional speckle reduction methods are fast and their memory consumption is insignificant. However, they are either good at smoothing homogeneous regions or preserving edges and point scatterers. State of the art despeckling methods are proposed to overcome this trade-off. However, they introduce another trade-off between denoising quality and resource consumption, thereby higher denoising quality requires higher computational load and/or memory consumption. In this paper, a local pixel-based total variation (TV) approach is proposed, which combines l(2)-norm and l(1)-norm in order to improve despeckling quality while keeping execution times reasonably short. Pixel-based approach allows efficient computation model with relatively low memory consumption. Their parallel implementations are also more efficient comparing to global TV approaches which generally require numerical solution of sparse linear systems. However, pixel-based approaches are trapped to local minima frequently hence despeckling quality is worse comparing to global TV approaches. Proposed method, namely mixed norm despeckling (MND), combines l(2)-norm and l(1)-norm in order to improve despeckling performance by alleviating local minima problem. All steps of the MND are parallelized using OpenMP on CPU and CUDA on GPU. Speckle reduction performance, execution time and memory consumption of the proposed method are shown using synthetic images and TerraSAR-X spot mode SAR images.en_US
dc.description.sponsorshipSPIEen_US
dc.description.sponsorshipScientific Research Coordination Unit of Yildirim Beyazit University [YBU-BAP-590]en_US
dc.description.sponsorshipThis work was supported by the Scientific Research Coordination Unit of Yildirim Beyazit University under Grant YBU-BAP-590.en_US
dc.identifier.doi10.1117/12.2067074
dc.identifier.isbn978-1-62841-310-6
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.scopus2-s2.0-84937899066en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1117/12.2067074
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6289
dc.identifier.volume9247en_US
dc.identifier.wosWOS:000348127200012en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpie-Int Soc Optical Engineeringen_US
dc.relation.ispartofHigh-Performance Computing in Remote Sensing Iven_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSynthetic aperture radaren_US
dc.subjectspeckle noiseen_US
dc.subjectdespecklingen_US
dc.subjectl(1)-normen_US
dc.subjectl(2)-normen_US
dc.subjecttotal variationen_US
dc.subjectsummed area tableen_US
dc.subjectOpenMPen_US
dc.subjectGPUen_US
dc.subjectCUDAen_US
dc.titleGPU efficient SAR image despeckling using mixed normsen_US
dc.typeConference Objecten_US

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