Convolutional Neural Networks for Spherical Signal Processing via Area-Regular Spherical Haar Tight Framelets
In this article, we develop a general theoretical framework for constructing Haar-type tight framelets on any compact set with a hierarchical partition. In particular, we construct a novel area-regular hierarchical partition on the two spheres and establish its corresponding spherical Haar tight framelets with directionality. We conclude by evaluating and illustrate the effectiveness of our area-regular spherical Haar tight framelets in several denoising experiments. Furthermore, we propose a convolutional neural network (CNN) model for spherical signal denoising, which employs fast framelet decomposition and reconstruction algorithms. Experiment results show that our proposed CNN model outperforms threshold methods and processes strong generalization and robustness.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:35 |
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| Enthalten in: |
IEEE transactions on neural networks and learning systems - 35(2024), 4 vom: 06. Apr., Seite 4400-4410 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Jianfei [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 06.04.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1109/TNNLS.2022.3160169 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM338795332 |
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| 520 | |a In this article, we develop a general theoretical framework for constructing Haar-type tight framelets on any compact set with a hierarchical partition. In particular, we construct a novel area-regular hierarchical partition on the two spheres and establish its corresponding spherical Haar tight framelets with directionality. We conclude by evaluating and illustrate the effectiveness of our area-regular spherical Haar tight framelets in several denoising experiments. Furthermore, we propose a convolutional neural network (CNN) model for spherical signal denoising, which employs fast framelet decomposition and reconstruction algorithms. Experiment results show that our proposed CNN model outperforms threshold methods and processes strong generalization and robustness | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Zhuang, Xiaosheng |e verfasserin |4 aut | |
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