  Recovering degraded low-resolution text images is challenging, especially for
Chinese text images with complex strokes and severe degradation in real-world
scenarios. Ensuring both text fidelity and style realness is crucial for
high-quality text image super-resolution. Recently, diffusion models have
achieved great success in natural image synthesis and restoration due to their
powerful data distribution modeling abilities and data generation capabilities.
In this work, we propose an Image Diffusion Model (IDM) to restore text images
with realistic styles. For diffusion models, they are not only suitable for
modeling realistic image distribution but also appropriate for learning text
distribution. Since text prior is important to guarantee the correctness of the
restored text structure according to existing arts, we also propose a Text
Diffusion Model (TDM) for text recognition which can guide IDM to generate text
images with correct structures. We further propose a Mixture of Multi-modality
module (MoM) to make these two diffusion models cooperate with each other in
all the diffusion steps. Extensive experiments on synthetic and real-world
datasets demonstrate that our Diffusion-based Blind Text Image Super-Resolution
(DiffTSR) can restore text images with more accurate text structures as well as
more realistic appearances simultaneously.
