A Novel Image Formation Model for Descattering
In the field of image descattering, the image formation models employed for restoration approaches are often simplified. In these models, scattering distribution is uniform in homogeneous media when transmission is fixed. Through specifically designed experiments, we discover that scattering exhibit...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 12 vom: 20. Dez., Seite 8173-8190 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Zusammenfassung: | In the field of image descattering, the image formation models employed for restoration approaches are often simplified. In these models, scattering distribution is uniform in homogeneous media when transmission is fixed. Through specifically designed experiments, we discover that scattering exhibits non-uniform characteristics even in homogeneous media. Neglecting non-uniform scattering in these models limits their accuracy in representing scattering distribution, resulting in existing image descattering approaches inadequate. To tackle these issues, this paper proposes a novel image formation model for image descattering, considering more physical parameters, such as zenith angle, azimuth angle, scattering phase function, and camera focal length. Our model describes the light transfer process in scattering media more accurately. For image descattering, we introduce corresponding algorithms for parameter estimation in our model and simultaneous restoration from degraded images. Experimental evaluations demonstrate the effectiveness of our proposed model in various tasks, including physical parameter estimation, pure-scattering removal, image dehazing, and underwater image restoration. In terms of calculating parameters, our results are close to the real values; in terms of underwater image restoration, our work outperforms the state-of-art methods; in terms of image dehazing, our work promotes the performance of existing methods by replacing previous models with our model |
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| Beschreibung: | Date Revised 08.11.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2024.3403234 |