Computer Science > Computer Vision and Pattern Recognition
arXiv:2312.10191 (cs)
[Submitted on 15 Dec 2023 (v1), last revised 16 Mar 2025 (this version, v3)]
Title:Tell Me What You See: Text-Guided Real-World Image Denoising
View a PDF of the paper titled Tell Me What You See: Text-Guided Real-World Image Denoising, by Erez Yosef and 1 other authors
View PDFHTML (experimental)Abstract: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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV) |
| Cite as: | arXiv:2312.10191 [cs.CV] |
| (orarXiv:2312.10191v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2312.10191 arXiv-issued DOI via DataCite |
Submission history
From: Erez Yosef [view email][v1] Fri, 15 Dec 2023 20:35:07 UTC (20,523 KB)
[v2] Wed, 29 May 2024 08:09:42 UTC (25,403 KB)
[v3] Sun, 16 Mar 2025 12:57:07 UTC (27,383 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled Tell Me What You See: Text-Guided Real-World Image Denoising, by Erez Yosef and 1 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.