TextOVSR: Text-Guided Real-World Opera Video Super-Resolution

• URL: http://arxiv.org/abs/2603.15153v1
• Date: Mon, 16 Mar 2026 11:48:43 GMT
• Title: TextOVSR: Text-Guided Real-World Opera Video Super-Resolution
• Abstract summary: Classic opera videos exhibit poor visual quality due to limitations of early filming equipment and long-term degradation during storage.<n>We propose a Text-guided Dual-Branch Opera Video Super-Resolution network, which introduces two types of textual prompts to guide the super-resolution process.<n> Experiments on our OperaLQ benchmark show that TextOVSR outperforms state-of-the-art methods both qualitatively and quantitatively.
• Score: 72.569334239113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many classic opera videos exhibit poor visual quality due to the limitations of early filming equipment and long-term degradation during storage. Although real-world video super-resolution (RWVSR) has achieved significant advances in recent years, directly applying existing methods to degraded opera videos remains challenging. The difficulties are twofold. First, accurately modeling real-world degradations is complex: simplistic combinations of classical degradation kernels fail to capture the authentic noise distribution, while methods that extract real noise patches from external datasets are prone to style mismatches that introduce visual artifacts. Second, current RWVSR methods, which rely solely on degraded image features, struggle to reconstruct realistic and detailed textures due to a lack of high-level semantic guidance. To address these issues, we propose a Text-guided Dual-Branch Opera Video Super-Resolution (TextOVSR) network, which introduces two types of textual prompts to guide the super-resolution process. Specifically, degradation-descriptive text, derived from the degradation process, is incorporated into the negative branch to constrain the solution space. Simultaneously, content-descriptive text is incorporated into a positive branch and our proposed Text-Enhanced Discriminator (TED) to provide semantic guidance for enhanced texture reconstruction. Furthermore, we design a Degradation-Robust Feature Fusion (DRF) module to facilitate cross-modal feature fusion while suppressing degradation interference. Experiments on our OperaLQ benchmark show that TextOVSR outperforms state-of-the-art methods both qualitatively and quantitatively. The code is available at https://github.com/ChangHua0/TextOVSR.

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