Bridging the semantic-visual gap: a universal super-resolution framework for ancient stele inscriptions

Ancient stele inscriptions face irreversible information loss due to millennia of environmental degradation. This paper presents the first systematic effort to address the task of Stele Inscription Text Image Super-Resolution. General-purpose models often suffer from a “semantic-visual imbalance” in this domain, where a lack of structural priors leads to spurious topological connections that render restored text illegible. To resolve this, we propose SISTR, the first systematic benchmark and progressive paradigm that bridges the gap between synthetic noise and real-world damage via LLM-driven structural decoupling and LVM-guided style injection. Furthermore, we introduce SteleSR, a universal restoration framework that integrates Edge-aware and Text Prior losses into diverse super-resolution backbones to ensure both visual authenticity and semantic legibility. Extensive evaluations across semantic and structural metrics demonstrate that SteleSR consistently enhances various models, establishing a pioneering standard for the high-fidelity digital preservation of historical artifacts. The code and data are available at https://github.com/liamhou123/SteleSR .