Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Myllykoski, Mirko | |
dc.contributor.author | Glowinski, Roland | |
dc.contributor.author | Kärkkäinen, Tommi | |
dc.contributor.author | Rossi, Tuomo | |
dc.contributor.editor | Skala, Václav | |
dc.contributor.editor | Gavrilova, Marina | |
dc.date.accessioned | 2018-03-21T09:43:12Z | - |
dc.date.available | 2018-03-21T09:43:12Z | - |
dc.date.issued | 2015 | |
dc.identifier.citation | WSCG 2015: full papers proceedings: 23rd International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 119-128. | en |
dc.identifier.isbn | 978-80-86943-65-7 (print) | |
dc.identifier.isbn | 978-80-86943-61-9 (CD-ROM) | |
dc.identifier.issn | 2464–4617 (print) | |
dc.identifier.issn | 2464–4625 (CD-ROM) | |
dc.identifier.uri | wscg.zcu.cz/WSCG2015/CSRN-2501.pdf | |
dc.identifier.uri | http://hdl.handle.net/11025/29433 | |
dc.description.abstract | This paper presents a graphics processing unit (GPU) implementation of a recently published augmented Lagrangian based L1-mean curvature image denoising algorithm. The algorithm uses a particular alternating direction method of multipliers to reduce the related saddle-point problem to an iterative sequence of four simpler minimization problems. Two of these subproblems do not contain the derivatives of the unknown variables and can therefore be solved point-wise without inter-process communication. In particular, this facilitates the efficient solution of the subproblem that deals with the non-convex term in the original objective function by modern GPUs. The two remaining subproblems are solved using the conjugate gradient method and a partial solution variant of the cyclic reduction method, both of which can be implemented relatively efficiently on GPUs. The numerical results indicate up to 33-fold speedups when compared against a single-threaded CPU implementation. The pointwise treated subproblem that takes care of the non-convex term in the original objective function was solved up to 76 times faster. | en |
dc.format | 10 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Václav Skala - UNION Agency | cs |
dc.relation.ispartofseries | WSCG 2015: full papers proceedings | en |
dc.rights | © Václav Skala - UNION Agency | en |
dc.subject | rozšířená Lagrangianova metoda | cs |
dc.subject | GPU výpočty | cs |
dc.subject | odstranění šumu z obrazu | cs |
dc.subject | zpracování obrazu | cs |
dc.subject | střední zakřivení | cs |
dc.subject | OpenCL | cs |
dc.title | A GPU-accelerated augmented Lagrangian based L1-mean curvature Image denoising algorithm implementation | en |
dc.type | konferenční příspěvek | cs |
dc.type | conferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.subject.translated | augmented Lagrangian method | en |
dc.subject.translated | GPU computing | en |
dc.subject.translated | image denoising | en |
dc.subject.translated | image processing | en |
dc.subject.translated | mean curvature | en |
dc.subject.translated | OpenCL | en |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | WSCG 2015: Full Papers Proceedings |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
Myllykoski.pdf | Plný text | 3,16 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/29433
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