dc.contributor.advisor | Ramstad, Tor Audun | nb_NO |
dc.contributor.author | Johansen, Michael | nb_NO |
dc.date.accessioned | 2014-12-19T13:47:03Z | |
dc.date.accessioned | 2015-12-22T11:46:00Z | |
dc.date.available | 2014-12-19T13:47:03Z | |
dc.date.available | 2015-12-22T11:46:00Z | |
dc.date.created | 2012-01-06 | nb_NO |
dc.date.issued | 2011 | nb_NO |
dc.identifier | 473496 | nb_NO |
dc.identifier.uri | http://hdl.handle.net/11250/2370359 | |
dc.description.abstract | In this thesis an application named ImageLab have been developed. Image-Lab is an image filtering application focusing on the use of nonlinear filteringprocesses. During development of the software it became clear that ImageLabis not a full image processing suite, such as Photoshop , but rather a complementto existing software solutions. This is true for now, but further developmentof the software may lead it to become an alternative full featured image processingsolution.Theory behind the use of ImageLab s image processing techniques was introducedin section 2, the same techniques was also introduced as pseudocode.Also the different filters and their functionality were explained. In section4.2 the same pseudo-code were turned into usable C#-code. Following thisrecipe it should be a straightforward process to duplicate the work done in thisthesis, both for novel applications or extending the current application further.In section 5 it was shown how the different filters performed with respect toan objective metric. While not being able to generally conclude a given filtersproficiency at removing noise, we can conclude that for a given set of initial conditions (input image, the type/strength of noise and parameters) some filters didperform better than others. With 2-sided variable height impulsive noise andthe Lena reference image a median-filter and a Distance Weighted Median-filterperformed the best (according to the objective Mean Square Error). All the filtersdid improve the objective metric, the worst (Mean 11-by-11) was able toremove more than 40%4 of the noise. The best filter (Median 1-by-1) was ableto remove more than 90%5 of the noise. | nb_NO |
dc.language | eng | nb_NO |
dc.publisher | Institutt for elektronikk og telekommunikasjon | nb_NO |
dc.subject | ntnudaim:4673 | no_NO |
dc.title | Nonlinear filtering in digtal image processing | nb_NO |
dc.type | Master thesis | nb_NO |
dc.source.pagenumber | 49 | nb_NO |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for elektronikk og telekommunikasjon | nb_NO |