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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.23533 |
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| _version_ | 1866910678893199360 |
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| author | Gilles, Jerome Tran, Giang Osher, Stanley |
| author_facet | Gilles, Jerome Tran, Giang Osher, Stanley |
| contents | A recently developed new approach, called ``Empirical Wavelet Transform'', aims to build 1D adaptive wavelet frames accordingly to the analyzed signal. In this paper, we present several extensions of this approach to 2D signals (images). We revisit some well-known transforms (tensor wavelets, Littlewood-Paley wavelets, ridgelets and curvelets) and show that it is possible to build their empirical counterpart. We prove that such constructions lead to different adaptive frames which show some promising properties for image analysis and processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23533 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 2D Empirical Transforms. Wavelets, Ridgelets and Curvelets revisited Gilles, Jerome Tran, Giang Osher, Stanley Functional Analysis Computer Vision and Pattern Recognition Image and Video Processing A recently developed new approach, called ``Empirical Wavelet Transform'', aims to build 1D adaptive wavelet frames accordingly to the analyzed signal. In this paper, we present several extensions of this approach to 2D signals (images). We revisit some well-known transforms (tensor wavelets, Littlewood-Paley wavelets, ridgelets and curvelets) and show that it is possible to build their empirical counterpart. We prove that such constructions lead to different adaptive frames which show some promising properties for image analysis and processing. |
| title | 2D Empirical Transforms. Wavelets, Ridgelets and Curvelets revisited |
| topic | Functional Analysis Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2410.23533 |