Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution
Scene text image super-resolution (STISR) aims to recover visually plausible appearance while preserving character semantics from degraded inputs. Existing STISR systems often rely on externally generated priors or separate image and text models, resulting in error propagation and costly multi-stage inference. We present DualTSR, a unified framework that formulates STISR as coupled continuous-discrete generation. Conditional flow matching restores continuous image latents, while absorbing-state discrete diffusion reconstructs text tokens. Both processes share a multimodal transformer backbone, allowing the evolving image and text states to interact throughout generation without an externa...
| Authors | Axi Niu, Knag Zhang, Qingsen Yan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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