Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise
In this article, we propose a novel group-based sparse representation (GSR) model for restoring color images in the presence of multiplicative noise. This model consists of a convex data-fidelity term, and two regularizations including GSR and saturation-value-based total variation (SVTV). The data-...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-02-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2024294?viewType=HTML |
| _version_ | 1827344872915337216 |
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| author | Miyoun Jung |
| author_facet | Miyoun Jung |
| author_sort | Miyoun Jung |
| collection | DOAJ |
| description | In this article, we propose a novel group-based sparse representation (GSR) model for restoring color images in the presence of multiplicative noise. This model consists of a convex data-fidelity term, and two regularizations including GSR and saturation-value-based total variation (SVTV). The data-fidelity term is suitable for handling heavy multiplicative noise. GSR enables the retention of textures and details while sufficiently removing noise in smooth regions without producing the staircase artifacts engendered by total variation-based models. Furthermore, we introduce a multi-color channel-based GSR that involves coupling between three color channels. This avoids the generation of color artifacts caused by decoupled color channel-based methods. SVTV further improves the visual quality of restored images by diminishing certain artifacts induced by patch-based methods. To solve the proposed nonconvex model and its subproblem, we exploit the alternating direction method of multipliers, which contributes to an efficient iterative algorithm. Numerical results demonstrate the outstanding performance of the proposed model compared to other existing models regarding visual aspect and image quality evaluation values. |
| first_indexed | 2024-03-07T22:55:53Z |
| format | Article |
| id | doaj.art-03cf38802a6d44928ce965b9526561b7 |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-03-07T22:55:53Z |
| publishDate | 2024-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-03cf38802a6d44928ce965b9526561b72024-02-23T01:16:28ZengAIMS PressAIMS Mathematics2473-69882024-02-01936013604010.3934/math.2024294Group sparse representation and saturation-value total variation based color image denoising under multiplicative noiseMiyoun Jung 0Department of Mathematics, Hankuk University of Foreign Studies, Yongin, 17035, KoreaIn this article, we propose a novel group-based sparse representation (GSR) model for restoring color images in the presence of multiplicative noise. This model consists of a convex data-fidelity term, and two regularizations including GSR and saturation-value-based total variation (SVTV). The data-fidelity term is suitable for handling heavy multiplicative noise. GSR enables the retention of textures and details while sufficiently removing noise in smooth regions without producing the staircase artifacts engendered by total variation-based models. Furthermore, we introduce a multi-color channel-based GSR that involves coupling between three color channels. This avoids the generation of color artifacts caused by decoupled color channel-based methods. SVTV further improves the visual quality of restored images by diminishing certain artifacts induced by patch-based methods. To solve the proposed nonconvex model and its subproblem, we exploit the alternating direction method of multipliers, which contributes to an efficient iterative algorithm. Numerical results demonstrate the outstanding performance of the proposed model compared to other existing models regarding visual aspect and image quality evaluation values.https://www.aimspress.com/article/doi/10.3934/math.2024294?viewType=HTMLcolor image denoisingmultiplicative noisegroup-based sparse representationsaturation-value total variationalternating direction method of multipliers |
| spellingShingle | Miyoun Jung Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise AIMS Mathematics color image denoising multiplicative noise group-based sparse representation saturation-value total variation alternating direction method of multipliers |
| title | Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise |
| title_full | Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise |
| title_fullStr | Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise |
| title_full_unstemmed | Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise |
| title_short | Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise |
| title_sort | group sparse representation and saturation value total variation based color image denoising under multiplicative noise |
| topic | color image denoising multiplicative noise group-based sparse representation saturation-value total variation alternating direction method of multipliers |
| url | https://www.aimspress.com/article/doi/10.3934/math.2024294?viewType=HTML |
| work_keys_str_mv | AT miyounjung groupsparserepresentationandsaturationvaluetotalvariationbasedcolorimagedenoisingundermultiplicativenoise |