Tell Me What You See: Text-Guided Real-World Image Denoising
Erez Yosef Raja Giryes
Image reconstruction in low-light conditions is a challenging problem. Many
solutions have been proposed for it, where the main approach is trying to learn
a good prior of natural images along with modeling the true statistics of the
noise in the scene. In the presence of very low lighting conditions, such
approaches are usually not enough, and additional information is required,
e.g., in the form of using multiple captures. In this work, we suggest as an
alternative to add a description of the scene as prior, which can be easily
done by the photographer who is capturing the scene. Using a text-conditioned
diffusion model, we show that adding image caption information improves
significantly the image reconstruction in low-light conditions on both
synthetic and real-world images.