Recent works have explored reference-based super-resolution (RefSR) to mitigate hallucinations in diffusion-based image restoration. A key challenge is that real-world degradations make correspondences between low-quality (LQ) inputs and reference (Ref) images unreliable, requiring adaptive control of reference usage. Existing methods either ignore LQ-Ref correlations or rely on brittle explicit matching, leading to over-reliance on misleading references or under-utilization of valuable cues. To address this, we propose Ada-RefSR, a single-step diffusion framework guided by a "Trust but Verify" principle: reference information is leveraged when reliable and suppressed otherwise. Its core component, Adaptive Implicit Correlation Gating (AICG), employs learnable summary tokens to distill dominant reference patterns and capture implicit correlations with LQ features. Integrated into the attention backbone, AICG provides lightweight, adaptive regulation of reference guidance, serving as a built-in safeguard against erroneous fusion. Experiments on multiple datasets demonstrate that Ada-RefSR achieves a strong balance of fidelity, naturalness, and efficiency, while remaining robust under varying reference alignment.

翻译：近期研究探索了基于参考的超分辨率方法，以缓解基于扩散的图像恢复中的幻觉问题。关键挑战在于现实世界的退化使得低质量输入与参考图像之间的对应关系不可靠，需要自适应地控制参考信息的使用。现有方法要么忽略低质量-参考相关性，要么依赖脆弱的显式匹配，导致对误导性参考的过度依赖或对有价值线索的利用不足。为解决此问题，我们提出Ada-RefSR，一个遵循“信任但验证”原则的单步扩散框架：在参考信息可靠时加以利用，否则予以抑制。其核心组件自适应隐式相关门控通过可学习的摘要令牌提炼主导参考模式，并捕获与低质量特征的隐式相关性。该组件集成于注意力主干网络中，以轻量级方式自适应调节参考引导，作为防止错误融合的内置保障机制。在多数据集上的实验表明，Ada-RefSR在保真度、自然度与效率之间实现了良好平衡，并在不同参考对齐条件下保持鲁棒性。