Geometry of the Visual Cortex with Applications to Image Inpainting and Enhancement
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866929606626377728 |
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| author | Ballerin, Francesco Grong, Erlend |
| author_facet | Ballerin, Francesco Grong, Erlend |
| contents | Equipping the rototranslation group $SE(2)$ with a sub-Riemannian structure inspired by the visual cortex V1, we propose algorithms for image inpainting and enhancement based on hypoelliptic diffusion. We innovate on previous implementations of the methods by Citti, Sarti, and Boscain et al., by proposing an alternative that prevents fading and is capable of producing sharper results in a procedure that we call WaxOn-WaxOff. We also exploit the sub-Riemannian structure to define a completely new unsharp filter using $SE(2)$, analogous to the classical unsharp filter for 2D image processing. We demonstrate our method on blood vessels enhancement in retinal scans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_07652 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Geometry of the Visual Cortex with Applications to Image Inpainting and Enhancement Ballerin, Francesco Grong, Erlend Computer Vision and Pattern Recognition Equipping the rototranslation group $SE(2)$ with a sub-Riemannian structure inspired by the visual cortex V1, we propose algorithms for image inpainting and enhancement based on hypoelliptic diffusion. We innovate on previous implementations of the methods by Citti, Sarti, and Boscain et al., by proposing an alternative that prevents fading and is capable of producing sharper results in a procedure that we call WaxOn-WaxOff. We also exploit the sub-Riemannian structure to define a completely new unsharp filter using $SE(2)$, analogous to the classical unsharp filter for 2D image processing. We demonstrate our method on blood vessels enhancement in retinal scans. |
| title | Geometry of the Visual Cortex with Applications to Image Inpainting and Enhancement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.07652 |