  Blind text image super-resolution (SR) is challenging as one needs to cope
with diverse font styles and unknown degradation. To address the problem,
existing methods perform character recognition in parallel to regularize the SR
task, either through a loss constraint or intermediate feature condition.
Nonetheless, the high-level prior could still fail when encountering severe
degradation. The problem is further compounded given characters of complex
structures, e.g., Chinese characters that combine multiple pictographic or
ideographic symbols into a single character. In this work, we present a novel
prior that focuses more on the character structure. In particular, we learn to
encapsulate rich and diverse structures in a StyleGAN and exploit such
generative structure priors for restoration. To restrict the generative space
of StyleGAN so that it obeys the structure of characters yet remains flexible
in handling different font styles, we store the discrete features for each
character in a codebook. The code subsequently drives the StyleGAN to generate
high-resolution structural details to aid text SR. Compared to priors based on
character recognition, the proposed structure prior exerts stronger
character-specific guidance to restore faithful and precise strokes of a
designated character. Extensive experiments on synthetic and real datasets
demonstrate the compelling performance of the proposed generative structure
prior in facilitating robust text SR.
