Refining Image Edge Detection via Linear Canonical Riesz Transforms
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916652565659648 |
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| author | Yang, Shuhui Fu, Zunwei Yang, Dachun Lin, Yan Li, Zhen |
| author_facet | Yang, Shuhui Fu, Zunwei Yang, Dachun Lin, Yan Li, Zhen |
| contents | Combining the linear canonical transform and the Riesz transform, we introduce the linear canonical Riesz transform (for short, LCRT), which is further proved to be a linear canonical multiplier. Using this LCRT multiplier, we conduct numerical simulations on images. Notably, the LCRT multiplier significantly reduces the complexity of the algorithm. Based on these we introduce the new concept of the sharpness $R^{\rm E}_{\rm sc}$ of the edge strength and continuity of images associated with the LCRT and, using it, we propose a new LCRT image edge detection method (for short, LCRT-IED method) and provide its mathematical foundation. Our experiments indicate that this sharpness $R^{\rm E}_{\rm sc}$ characterizes the macroscopic trend of edge variations of the image under consideration, while this new LCRT-IED method not only controls the overall edge strength and continuity of the image, but also excels in feature extraction in some local regions. These highlight the fundamental differences between the LCRT and the Riesz transform, which are precisely due to the multiparameter of the former. This new LCRT-IED method might be of significant importance for image feature extraction, image matching, and image refinement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11148 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Refining Image Edge Detection via Linear Canonical Riesz Transforms Yang, Shuhui Fu, Zunwei Yang, Dachun Lin, Yan Li, Zhen Functional Analysis Primary 42B20, Secondary 42B35, 42A38, 94A08 Combining the linear canonical transform and the Riesz transform, we introduce the linear canonical Riesz transform (for short, LCRT), which is further proved to be a linear canonical multiplier. Using this LCRT multiplier, we conduct numerical simulations on images. Notably, the LCRT multiplier significantly reduces the complexity of the algorithm. Based on these we introduce the new concept of the sharpness $R^{\rm E}_{\rm sc}$ of the edge strength and continuity of images associated with the LCRT and, using it, we propose a new LCRT image edge detection method (for short, LCRT-IED method) and provide its mathematical foundation. Our experiments indicate that this sharpness $R^{\rm E}_{\rm sc}$ characterizes the macroscopic trend of edge variations of the image under consideration, while this new LCRT-IED method not only controls the overall edge strength and continuity of the image, but also excels in feature extraction in some local regions. These highlight the fundamental differences between the LCRT and the Riesz transform, which are precisely due to the multiparameter of the former. This new LCRT-IED method might be of significant importance for image feature extraction, image matching, and image refinement. |
| title | Refining Image Edge Detection via Linear Canonical Riesz Transforms |
| topic | Functional Analysis Primary 42B20, Secondary 42B35, 42A38, 94A08 |
| url | https://arxiv.org/abs/2503.11148 |