We design a new conformal prediction method with theoretical guarantees that produces 'confidence masks' for uncertainty quantification of image restoration tasks such as image super-resolution.

We propose TADiSR, a text-aware diffusion model with joint image super-resolution and segmentation decoders, achieving accurate full-image text SR under real-world degradations.