  Current Scene text image super-resolution approaches primarily focus on
extracting robust features, acquiring text information, and complex training
strategies to generate super-resolution images. However, the upsampling module,
which is crucial in the process of converting low-resolution images to
high-resolution ones, has received little attention in existing works. To
address this issue, we propose the Pixel Adapter Module (PAM) based on graph
attention to address pixel distortion caused by upsampling. The PAM effectively
captures local structural information by allowing each pixel to interact with
its neighbors and update features. Unlike previous graph attention mechanisms,
our approach achieves 2-3 orders of magnitude improvement in efficiency and
memory utilization by eliminating the dependency on sparse adjacency matrices
and introducing a sliding window approach for efficient parallel computation.
Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for
robust feature extraction from text images, and a Local Contour Awareness loss
($\mathcal{L}_{lca}$) to enhance the model's perception of details.
Comprehensive experiments on TextZoom demonstrate that our proposed method
generates high-quality super-resolution images, surpassing existing methods in
recognition accuracy. For single-stage and multi-stage strategies, we achieved
improvements of 0.7\% and 2.6\%, respectively, increasing the performance from
52.6\% and 53.7\% to 53.3\% and 56.3\%. The code is available at
https://github.com/wenyu1009/RTSRN.
