A novel weighted total variation model for image denoising
Abstract Image denoising is a very important problem in image processing field. In order to improve denoising effects and meanwhile keep image structures, a novel weighted total variation (WTV) model is proposed in this paper. The WTV model consists of data fidelity and ℓ1 norm based regularisation...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-10-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12259 |
| _version_ | 1797991588635344896 |
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| author | Meng‐Meng Li Bing‐Zhao Li |
| author_facet | Meng‐Meng Li Bing‐Zhao Li |
| author_sort | Meng‐Meng Li |
| collection | DOAJ |
| description | Abstract Image denoising is a very important problem in image processing field. In order to improve denoising effects and meanwhile keep image structures, a novel weighted total variation (WTV) model is proposed in this paper. The WTV model consists of data fidelity and ℓ1 norm based regularisation terms. In the WTV model, a weight function w in exponential form is incorporated into the regularisation term, which only depends on the given image itself without extra parameters. The nonlinearly monotone formulation of w helps to increase gaps between lower and higher frequencies of images, which is effective to highlight edges and keep textures. For solving the proposed model, the alternating direction method of multipliers is explored and the according convergence is analysed. Compared experiments of TV, HOTV, ATV and TVp models are conducted and the results show the effectiveness and efficiency of the proposed model. |
| first_indexed | 2024-04-11T08:53:29Z |
| format | Article |
| id | doaj.art-f602e128ea96476489e5f0e856b570c3 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-11T08:53:29Z |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-f602e128ea96476489e5f0e856b570c32022-12-22T04:33:21ZengWileyIET Image Processing1751-96591751-96672021-10-0115122749276010.1049/ipr2.12259A novel weighted total variation model for image denoisingMeng‐Meng Li0Bing‐Zhao Li1School of Mathematics and Statistics Beijing Institute of Technology Beijing P. R. ChinaSchool of Mathematics and Statistics Beijing Institute of Technology Beijing P. R. ChinaAbstract Image denoising is a very important problem in image processing field. In order to improve denoising effects and meanwhile keep image structures, a novel weighted total variation (WTV) model is proposed in this paper. The WTV model consists of data fidelity and ℓ1 norm based regularisation terms. In the WTV model, a weight function w in exponential form is incorporated into the regularisation term, which only depends on the given image itself without extra parameters. The nonlinearly monotone formulation of w helps to increase gaps between lower and higher frequencies of images, which is effective to highlight edges and keep textures. For solving the proposed model, the alternating direction method of multipliers is explored and the according convergence is analysed. Compared experiments of TV, HOTV, ATV and TVp models are conducted and the results show the effectiveness and efficiency of the proposed model.https://doi.org/10.1049/ipr2.12259 |
| spellingShingle | Meng‐Meng Li Bing‐Zhao Li A novel weighted total variation model for image denoising IET Image Processing |
| title | A novel weighted total variation model for image denoising |
| title_full | A novel weighted total variation model for image denoising |
| title_fullStr | A novel weighted total variation model for image denoising |
| title_full_unstemmed | A novel weighted total variation model for image denoising |
| title_short | A novel weighted total variation model for image denoising |
| title_sort | novel weighted total variation model for image denoising |
| url | https://doi.org/10.1049/ipr2.12259 |
| work_keys_str_mv | AT mengmengli anovelweightedtotalvariationmodelforimagedenoising AT bingzhaoli anovelweightedtotalvariationmodelforimagedenoising AT mengmengli novelweightedtotalvariationmodelforimagedenoising AT bingzhaoli novelweightedtotalvariationmodelforimagedenoising |