Taming Stable Diffusion for Computed Tomography Blind Super-Resolution
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
High-resolution computed tomography (CT) imaging is essential for medical
diagnosis but requires increased radiation exposure, creating a critical
trade-off between image quality and patient safety. While deep learning methods
have shown promise in CT super-resolution, they face challenges with complex
degradations and limited medical training data. Meanwhile, large-scale
pre-trained diffusion models, particularly Stable Diffusion, have demonstrated
remarkable capabilities in synthesizing fine details across various vision
tasks. Motivated by this, we propose a novel framework that adapts Stable
Diffusion for CT blind super-resolution. We employ a practical degradation
model to synthesize realistic low-quality images and leverage a pre-trained
vision-language model to generate corresponding descriptions. Subsequently, we
perform super-resolution using Stable Diffusion with a specialized controlling
strategy, conditioned on both low-resolution inputs and the generated text
descriptions. Extensive experiments show that our method outperforms existing
approaches, demonstrating its potential for achieving high-quality CT imaging
at reduced radiation doses. Our code will be made publicly available.
