TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image
  Super-Resolution
Baolin Liu Zongyuan Yang Pengfei Wang Junjie Zhou Ziqi Liu Ziyi Song Yan Liu Yongping Xiong
The goal of scene text image super-resolution is to reconstruct
high-resolution text-line images from unrecognizable low-resolution inputs. The
existing methods relying on the optimization of pixel-level loss tend to yield
text edges that exhibit a notable degree of blurring, thereby exerting a
substantial impact on both the readability and recognizability of the text. To
address these issues, we propose TextDiff, the first diffusion-based framework
tailored for scene text image super-resolution. It contains two modules: the
Text Enhancement Module (TEM) and the Mask-Guided Residual Diffusion Module
(MRD). The TEM generates an initial deblurred text image and a mask that
encodes the spatial location of the text. The MRD is responsible for
effectively sharpening the text edge by modeling the residuals between the
ground-truth images and the initial deblurred images. Extensive experiments
demonstrate that our TextDiff achieves state-of-the-art (SOTA) performance on
public benchmark datasets and can improve the readability of scene text images.
Moreover, our proposed MRD module is plug-and-play that effectively sharpens
the text edges produced by SOTA methods. This enhancement not only improves the
readability and recognizability of the results generated by SOTA methods but
also does not require any additional joint training. Available
Codes:https://github.com/Lenubolim/TextDiff.