Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave Penalty
The acquisition and transmission of magnetic resonance images are susceptible to noise, particularly impulse noise. Although the method based on the ℓ0-norm and overlapping group sparse total variation (ℓ0-OGSTV) is effective for impulse noise image restoration, it can only mitigate the staircase a...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Slovenian Society for Stereology and Quantitative Image Analysis
2024-03-01
|
| Series: | Image Analysis and Stereology |
| Subjects: | |
| Online Access: | https://www.ias-iss.org/ojs/IAS/article/view/3139 |
| _version_ | 1797216357216944128 |
|---|---|
| author | Wei Xue Yumeng Ge Xiaolei Gu Xuan Qi Tao Tao |
| author_facet | Wei Xue Yumeng Ge Xiaolei Gu Xuan Qi Tao Tao |
| author_sort | Wei Xue |
| collection | DOAJ |
| description |
The acquisition and transmission of magnetic resonance images are susceptible to noise, particularly impulse noise. Although the method based on the ℓ0-norm and overlapping group sparse total variation (ℓ0-OGSTV) is effective for impulse noise image restoration, it can only mitigate the staircase artifacts to a certain extent. To boost the impulse noise removal performance of ℓ0-OGSTV, we propose a new restoration model that consists of two terms. Specifically, in the first term, we keep using the ℓ0-norm as the data fidelity term to eliminate impulse noise. In the second term, we first introduce an overlapping group sparsity fractional-order total variation regularizer to eliminate staircase artifacts while preserving structural information. Then, we adopt the minimax-concave penalty to further accurately estimate the image edges. Finally, we employ an alternate direction method of multipliers to solve the proposed optimization model. Clinical experiments demonstrate its effectiveness in denoising medical images.
|
| first_indexed | 2024-04-24T11:44:40Z |
| format | Article |
| id | doaj.art-45e8c5eddd3a4e29a572bee9cf2b94b9 |
| institution | Directory Open Access Journal |
| issn | 1580-3139 1854-5165 |
| language | English |
| last_indexed | 2024-04-24T11:44:40Z |
| publishDate | 2024-03-01 |
| publisher | Slovenian Society for Stereology and Quantitative Image Analysis |
| record_format | Article |
| series | Image Analysis and Stereology |
| spelling | doaj.art-45e8c5eddd3a4e29a572bee9cf2b94b92024-04-09T11:16:56ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652024-03-0143110.5566/ias.3139Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave PenaltyWei XueYumeng GeXiaolei GuXuan QiTao Tao The acquisition and transmission of magnetic resonance images are susceptible to noise, particularly impulse noise. Although the method based on the ℓ0-norm and overlapping group sparse total variation (ℓ0-OGSTV) is effective for impulse noise image restoration, it can only mitigate the staircase artifacts to a certain extent. To boost the impulse noise removal performance of ℓ0-OGSTV, we propose a new restoration model that consists of two terms. Specifically, in the first term, we keep using the ℓ0-norm as the data fidelity term to eliminate impulse noise. In the second term, we first introduce an overlapping group sparsity fractional-order total variation regularizer to eliminate staircase artifacts while preserving structural information. Then, we adopt the minimax-concave penalty to further accurately estimate the image edges. Finally, we employ an alternate direction method of multipliers to solve the proposed optimization model. Clinical experiments demonstrate its effectiveness in denoising medical images. https://www.ias-iss.org/ojs/IAS/article/view/3139fractional-order total variationimage denoisingℓ0-normminimax-concave penaltyoverlapping group sparsity |
| spellingShingle | Wei Xue Yumeng Ge Xiaolei Gu Xuan Qi Tao Tao Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave Penalty Image Analysis and Stereology fractional-order total variation image denoising ℓ0-norm minimax-concave penalty overlapping group sparsity |
| title | Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave Penalty |
| title_full | Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave Penalty |
| title_fullStr | Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave Penalty |
| title_full_unstemmed | Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave Penalty |
| title_short | Boosting MR Image Impulse Noise Removal With Overlapping Group Sparse Fractional-Order Total Variation and Minimax Concave Penalty |
| title_sort | boosting mr image impulse noise removal with overlapping group sparse fractional order total variation and minimax concave penalty |
| topic | fractional-order total variation image denoising ℓ0-norm minimax-concave penalty overlapping group sparsity |
| url | https://www.ias-iss.org/ojs/IAS/article/view/3139 |
| work_keys_str_mv | AT weixue boostingmrimageimpulsenoiseremovalwithoverlappinggroupsparsefractionalordertotalvariationandminimaxconcavepenalty AT yumengge boostingmrimageimpulsenoiseremovalwithoverlappinggroupsparsefractionalordertotalvariationandminimaxconcavepenalty AT xiaoleigu boostingmrimageimpulsenoiseremovalwithoverlappinggroupsparsefractionalordertotalvariationandminimaxconcavepenalty AT xuanqi boostingmrimageimpulsenoiseremovalwithoverlappinggroupsparsefractionalordertotalvariationandminimaxconcavepenalty AT taotao boostingmrimageimpulsenoiseremovalwithoverlappinggroupsparsefractionalordertotalvariationandminimaxconcavepenalty |