Reconstructing Group Wavelet Transform From Feature Maps With a Reproducing Kernel Iteration
Copyright © 2022 Barbieri..
In this article, we consider the problem of reconstructing an image that is downsampled in the space of its SE(2) wavelet transform, which is motivated by classical models of simple cell receptive fields and feature preference maps in the primary visual cortex. We prove that, whenever the problem is solvable, the reconstruction can be obtained by an elementary project and replace iterative scheme based on the reproducing kernel arising from the group structure, and show numerical results on real images.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
Frontiers in computational neuroscience - 16(2022) vom: 20., Seite 775241 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Barbieri, Davide [VerfasserIn] |
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| Links: |
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| Themen: |
Harmonic analysis |
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| Anmerkungen: |
Date Revised 03.11.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fncom.2022.775241 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a In this article, we consider the problem of reconstructing an image that is downsampled in the space of its SE(2) wavelet transform, which is motivated by classical models of simple cell receptive fields and feature preference maps in the primary visual cortex. We prove that, whenever the problem is solvable, the reconstruction can be obtained by an elementary project and replace iterative scheme based on the reproducing kernel arising from the group structure, and show numerical results on real images | ||
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