| dc.date.accessioned |
2017-08-31T06:21:28Z |
und |
| dc.date.accessioned |
2017-10-24T12:22:14Z |
|
| dc.date.available |
2017-08-31T06:21:28Z |
und |
| dc.date.available |
2017-10-24T12:22:14Z |
|
| dc.date.issued |
2017-08-31T06:21:28Z |
|
| dc.identifier.uri |
http://radr.hulib.helsinki.fi/handle/10138.1/6147 |
und |
| dc.identifier.uri |
http://hdl.handle.net/10138.1/6147 |
|
| dc.title |
Non-local total variation in sparse and limited angle computerized tomography |
en |
| ethesis.discipline |
Applied Mathematics |
en |
| ethesis.discipline |
Soveltava matematiikka |
fi |
| ethesis.discipline |
Tillämpad matematik |
sv |
| ethesis.discipline.URI |
http://data.hulib.helsinki.fi/id/2646f59d-c072-44e7-b1c1-4e4b8b798323 |
|
| ethesis.department.URI |
http://data.hulib.helsinki.fi/id/61364eb4-647a-40e2-8539-11c5c0af8dc2 |
|
| ethesis.department |
Institutionen för matematik och statistik |
sv |
| ethesis.department |
Department of Mathematics and Statistics |
en |
| ethesis.department |
Matematiikan ja tilastotieteen laitos |
fi |
| ethesis.faculty |
Matematisk-naturvetenskapliga fakulteten |
sv |
| ethesis.faculty |
Matemaattis-luonnontieteellinen tiedekunta |
fi |
| ethesis.faculty |
Faculty of Science |
en |
| ethesis.faculty.URI |
http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca |
|
| ethesis.university.URI |
http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97 |
|
| ethesis.university |
Helsingfors universitet |
sv |
| ethesis.university |
University of Helsinki |
en |
| ethesis.university |
Helsingin yliopisto |
fi |
| dct.creator |
Djurabekova, Nargiza |
|
| dct.issued |
2017 |
|
| dct.language.ISO639-2 |
eng |
|
| dct.abstract |
Sparse and limited angle tomography are common techniques used in computerized tomography to understand the inner working of live and inanimate objects with a reduced radiation dose. Unfortunately due to the lack of full data, these problems are often severely ill-posed and need powerful regularization strategies to create good reconstructions. One such tool is total variation, which creates good but patchy reconstructions. In this paper we analyze the use of non-local total variation as a way to improve the reconstruction quality.
The analysis involves using both, simulated and real world data and comparing the TV reconstructions with the NLTV ones. The results leas us to believe that NLTV is particularly efficient and superior tool in sparse tomography, but further research is required to improve TV's limited-angle reconstructions. |
en |
| dct.language |
en |
|
| ethesis.language.URI |
http://data.hulib.helsinki.fi/id/languages/eng |
|
| ethesis.language |
English |
en |
| ethesis.language |
englanti |
fi |
| ethesis.language |
engelska |
sv |
| ethesis.thesistype |
pro gradu-avhandlingar |
sv |
| ethesis.thesistype |
pro gradu -tutkielmat |
fi |
| ethesis.thesistype |
master's thesis |
en |
| ethesis.thesistype.URI |
http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis |
|
| dct.identifier.urn |
URN:NBN:fi-fe2017112251690 |
|
| dc.type.dcmitype |
Text |
|
