MegaSR: 为真实世界图像超分辨率挖掘定制语义和表达引导
MegaSR: Mining Customized Semantics and Expressive Guidance for Real-World Image Super-Resolution
AI总结：
MegaSR通过定制语义和表达引导提升真实世界图像超分辨率的重建质量与结构一致性。
摘要：
文本到图像（T2I）模型由于其丰富的内部隐含知识，为真实世界图像超分辨率（Real-ISR）带来了新时代。尽管引入高层语义先验和密集像素引导在重建方面取得了进展，但我们通过分析现有T2I-based Real-ISR方法的行为，识别出几个关键现象：（1）细部不足，最终导致局部区域的不正确重建。（2）块级语义不一致，导致U-Net块间的语义解释分散。（3）边缘模糊，导致明显的结构退化。基于这些观察，我们首先引入MegaSR，通过细粒度定制语义和表达引导增强T2I-based Real-ISR模型，以解锁语义丰富且结构一致的重建。然后，我们提出定制语义模块（CSM）以从图像模态补充细粒度语义，并调节多级知识之间的语义融合，以实现不同U-Net块的定制。除了语义适应外，我们通过成对比较识别表达多模态信号，并引入多模态信号融合模块（MSFM）以聚合它们，以实现结构一致的重建。在真实世界和合成数据集上的广泛实验展示了该方法的优越性。值得注意的是，它不仅在质量驱动的指标上实现了最先进的性能，而且在保真度聚焦的指标上仍具有竞争力，平衡了感知现实与忠实内容重建。
英文摘要：
Text-to-image (T2I) models have ushered in a new era of real-world image super-resolution (Real-ISR) due to their rich internal implicit knowledge for multimodal learning. Although bringing high-level semantic priors and dense pixel guidance have led to advances in reconstruction, we identified several critical phenomena by analyzing the behavior of existing T2I-based Real-ISR methods: (1) Fine detail deficiency, which ultimately leads to incorrect reconstruction in local regions. (2) Block-wise semantic inconsistency, which results in distracted semantic interpretations across U-Net blocks. (3) Edge ambiguity, which causes noticeable structural degradation. Building upon these observations, we first introduce MegaSR, which enhances the T2I-based Real-ISR models with fine-grained customized semantics and expressive guidance to unlock semantically rich and structurally consistent reconstruction. Then, we propose the Customized Semantics Module (CSM) to supplement fine-grained semantics from the image modality and regulate the semantic fusion between multi-level knowledge to realize customization for different U-Net blocks. Besides the semantic adaptation, we identify expressive multimodal signals through pair-wise comparisons and introduce the Multimodal Signal Fusion Module (MSFM) to aggregate them for structurally consistent reconstruction. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of the method. Notably, it not only achieves state-of-the-art performance on quality-driven metrics but also remains competitive on fidelity-focused metrics, striking a balance between perceptual realism and faithful content reconstruction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
