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A LULU noise removal algorithm for images using principal component analysis
Dissertation (MSc)--University of Pretoria, 2018.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2019
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| _version_ | 1867613716763115520 |
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| access_status_str | Open Access |
| author2 | Fabris-Rotelli, Inger Nicolette |
| author_browse | Fabris-Rotelli, Inger Nicolette |
| author_facet | Fabris-Rotelli, Inger Nicolette |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2018. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/68706 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:34.602Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/68706 A LULU noise removal algorithm for images using principal component analysis Fabris-Rotelli, Inger Nicolette u10192205@tuks.co.za Papavarnavas, Christine UCTD Statistics Dissertation (MSc)--University of Pretoria, 2018. There are various methods to efficiently denoise an image, one method is Principle Component Analysis (PCA). Classical PCA reduces the dimensionality of a dataset, transforming the original dataset to preserve only significant principle components hence removing noise and trivial information from the image. The implementation of˘a PCA with local pixel grouping (LPG) in statistical signal processing, ensures that an image’s local features are effectively preserved and the noise removed. The LPG-PCA based denoising scheme investigated in Zhang et al. (Zhang, Dong, Zhang and Shi, 2010) is spatially adaptive and used a local window combined with LPG to extract similar training samples for PCA estimation. We propose using the LULU smoother with Zhang et al.’s LPG-PCA algorithm to remove noise from images corrupted with Gaussian, Gumbel and speckle noise. Our proposed LULU LPG-PCA algorithm is an improvement of Zhang et al.’s LPG-PCA algorithm. Since the resultant images and quality performance measure, the structural similarity index (SSIM) values obtained for the proposed LULU LPG-PCA algorithm were superior in comparison to Zhang et al.’s LPG-PCA algorithm for different noise types and varying noise levels. The results obtained highlight the versatile ability of the LULU smoother to tackle different noise types and noise levels when combined with Zhang et al.’s LPG-PCA algorithm. Our proposed LULU LPG-PCA algorithm achieves a very competitive denoising performance in comparison to Zhang et al.'s established LPG-PCA algorithm and in some cases outperforms it for different noise types when the noise corruption is more extensive, which is associated with greater noise levels. University of Pretoria National Research Foundation (NRF) Statistics MSc Unrestricted 2019-03-25T14:57:40Z 2019-03-25T14:57:40Z 2019-09 2018 Dissertation Papavarnavas, C 2018, A LULU noise removal algorithm for images using principal component analysis, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/68706> S2019 http://hdl.handle.net/2263/68706 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Statistics A LULU noise removal algorithm for images using principal component analysis |
| title | A LULU noise removal algorithm for images using principal component analysis |
| title_full | A LULU noise removal algorithm for images using principal component analysis |
| title_fullStr | A LULU noise removal algorithm for images using principal component analysis |
| title_full_unstemmed | A LULU noise removal algorithm for images using principal component analysis |
| title_short | A LULU noise removal algorithm for images using principal component analysis |
| title_sort | lulu noise removal algorithm for images using principal component analysis |
| topic | UCTD Statistics |
| url | http://hdl.handle.net/2263/68706 |