Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization
Abstract Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-65886-5 |
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| _version_ | 1832585722038583296 |
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| author | Siyuan Li Yuan Liu Jiafu Zeng Yepeng Liu Yue Li Qingsong Xie |
| author_facet | Siyuan Li Yuan Liu Jiafu Zeng Yepeng Liu Yue Li Qingsong Xie |
| author_sort | Siyuan Li |
| collection | DOAJ |
| description | Abstract Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details, adopting a multi-directional difference constrained global gradient sparsity decomposition method, which provides a guidance image with weaker texture detail gradients. Meanwhile, using the luminance channel as a reference, edge-aware operator is constructed based on local gradient constraints. This operator weakens the gradients of repetitive and similar texture details, enabling it to obtain more accurate structural information for guiding global optimization of the image. By projecting multi-directional differences onto the horizontal and vertical directions, a mapping from multi-directional differences to bi-directional gradients is achieved. Additionally, to ensure the consistency of measurement results, a multi-directional gradient normalization method is designed. Through experiments, we demonstrate that our method exhibits significant advantages in preserving image edges compared to current advanced smoothing methods. |
| format | Article |
| id | doaj-art-8b33a0ff5a7c47c285f443b7a49e74dd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8b33a0ff5a7c47c285f443b7a49e74dd2025-01-26T12:35:04ZengNature PortfolioScientific Reports2045-23222024-07-0114111410.1038/s41598-024-65886-5Image smoothing method based on global gradient sparsity and local relative gradient constraint optimizationSiyuan Li0Yuan Liu1Jiafu Zeng2Yepeng Liu3Yue Li4Qingsong Xie5School of Computer Science and Technology, Shandong Technology and Business UniversitySchool of Computer Science and Technology, Shandong Technology and Business UniversitySchool of Computer Science and Technology, Shandong Technology and Business UniversitySchool of Computer Science and Technology, Shandong Technology and Business UniversityYantai Growth Drivers Conversion Research Institute and Yantai Science and Technology Achievement Transfer and Transformation Demonstration BaseSchool of Computer Science and Technology, Shandong Technology and Business UniversityAbstract Removing texture while preserving the main structure of an image is a challenging task. To address this, this paper propose an image smoothing method based on global gradient sparsity and local relative gradient constraints optimization. To reduce the interference of complex texture details, adopting a multi-directional difference constrained global gradient sparsity decomposition method, which provides a guidance image with weaker texture detail gradients. Meanwhile, using the luminance channel as a reference, edge-aware operator is constructed based on local gradient constraints. This operator weakens the gradients of repetitive and similar texture details, enabling it to obtain more accurate structural information for guiding global optimization of the image. By projecting multi-directional differences onto the horizontal and vertical directions, a mapping from multi-directional differences to bi-directional gradients is achieved. Additionally, to ensure the consistency of measurement results, a multi-directional gradient normalization method is designed. Through experiments, we demonstrate that our method exhibits significant advantages in preserving image edges compared to current advanced smoothing methods.https://doi.org/10.1038/s41598-024-65886-5 |
| spellingShingle | Siyuan Li Yuan Liu Jiafu Zeng Yepeng Liu Yue Li Qingsong Xie Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization Scientific Reports |
| title | Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization |
| title_full | Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization |
| title_fullStr | Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization |
| title_full_unstemmed | Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization |
| title_short | Image smoothing method based on global gradient sparsity and local relative gradient constraint optimization |
| title_sort | image smoothing method based on global gradient sparsity and local relative gradient constraint optimization |
| url | https://doi.org/10.1038/s41598-024-65886-5 |
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