Tell Me What You See: Text-Guided Real-World Image Denoising
Image reconstruction from noisy sensor measurements is challenging and many methods have been proposed for it. Yet, most approaches focus on learning robust natural image priors while modeling the scene’s noise statistics. In extremely low-light conditions, these methods often remain insu...
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| Główni autorzy: | , |
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| Format: | Artykuł |
| Język: | angielski |
| Wydane: |
IEEE
2025-01-01
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| Seria: | IEEE Open Journal of Signal Processing |
| Hasła przedmiotowe: | |
| Dostęp online: | https://ieeexplore.ieee.org/document/11078899/ |
| Etykiety: |
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| Streszczenie: | Image reconstruction from noisy sensor measurements is challenging and many methods have been proposed for it. Yet, most approaches focus on learning robust natural image priors while modeling the scene’s noise statistics. In extremely low-light conditions, these methods often remain insufficient. Additional information is needed, such as multiple captures or, as suggested here, scene description. As an alternative, we propose using a text-based description of the scene as an additional prior, something the photographer can easily provide. Inspired by the remarkable success of text-guided diffusion models in image generation, we show that adding image caption information significantly improves image denoising and reconstruction for both synthetic and real-world images. All code and data will be made publicly available upon publication. |
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| ISSN: | 2644-1322 |