VOSR: A Vision-Only Generative Model for Image Super-Resolution
Most of the recent generative image super-resolution (SR) methods rely on adapting large text-to-image (T2I) diffusion models pretrained on web-scale text-image data. While effective, this paradigm starts from a generic T2I generator, despite that SR is fundamentally a low-resolution (LR) input-conditioned image restoration task. In this work, we investigate whether an SR model trained purely on visual data can rival T2I-based ones. To this end, we propose VOSR, a Vision-Only generative framework for SR. We first extract semantically rich and spatially grounded features from the LR input using a pretrained vision encoder as visual semantic guidance. We then revisit classifier-free guidanc...
| Authors | Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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